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Tejera-Nieves M, Seong DY, Reist L, Walker BJ. The Dynamic Assimilation Technique measures photosynthetic CO2 response curves with similar fidelity to steady-state approaches in half the time. JOURNAL OF EXPERIMENTAL BOTANY 2024; 75:2819-2828. [PMID: 38366564 PMCID: PMC11103103 DOI: 10.1093/jxb/erae057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 02/14/2024] [Indexed: 02/18/2024]
Abstract
The net CO2 assimilation (A) response to intercellular CO2 concentration (Ci) is a fundamental measurement in photosynthesis and plant physiology research. The conventional A/Ci protocols rely on steady-state measurements and take 15-40 min per measurement, limiting data resolution or biological replication. Additionally, there are several CO2 protocols employed across the literature, without clear consensus as to the optimal protocol or systematic biases in their estimations. We compared the non-steady-state Dynamic Assimilation Technique (DAT) protocol and the three most used CO2 protocols in steady-state measurements, and tested whether different CO2 protocols lead to systematic differences in estimations of the biochemical limitations to photosynthesis. The DAT protocol reduced the measurement time by almost half without compromising estimation accuracy or precision. The monotonic protocol was the fastest steady-state method. Estimations of biochemical limitations to photosynthesis were very consistent across all CO2 protocols, with slight differences in Rubisco carboxylation limitation. The A/Ci curves were not affected by the direction of the change of CO2 concentration but rather the time spent under triose phosphate utilization (TPU)-limited conditions. Our results suggest that the maximum rate of Rubisco carboxylation (Vcmax), linear electron flow for NADPH supply (J), and TPU measured using different protocols within the literature are comparable, or at least not systematically different based on the measurement protocol used.
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Affiliation(s)
- Mauricio Tejera-Nieves
- MSU-DOE Plant Research Laboratory, Michigan State University, 612 Wilson Rd, East Lansing, MI 48824, USA
- Great Lakes Bioenergy Research Center, Michigan State University, 1129 Farm Ln, East Lansing, MI 48824, USA
| | - Do Young Seong
- Department of Medical Informatics, College of Medicine, Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
| | - Lucas Reist
- Department of Biochemistry & Molecular Biology, Michigan State University, Molecular Plant Sciences Building, 1066 Bogue Street, East Lansing, MI 48824, USA
| | - Berkley J Walker
- MSU-DOE Plant Research Laboratory, Michigan State University, 612 Wilson Rd, East Lansing, MI 48824, USA
- Great Lakes Bioenergy Research Center, Michigan State University, 1129 Farm Ln, East Lansing, MI 48824, USA
- Department of Plant Biology, Michigan State University, 612 Wilson Rd, East Lansing, MI 48824, USA
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2
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Kenzhebayeva S, Mazkirat S, Shoinbekova S, Atabayeva S, Abekova A, Omirbekova N, Doktyrbay G, Asrandina S, Zharassova D, Amirova A, Serfling A. Phenotyping and Exploitation of Kompetitive Allele-Specific PCR Assays for Genes Underpinning Leaf Rust Resistance in New Spring Wheat Mutant Lines. Curr Issues Mol Biol 2024; 46:689-709. [PMID: 38248347 PMCID: PMC10814123 DOI: 10.3390/cimb46010045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 01/03/2024] [Accepted: 01/05/2024] [Indexed: 01/23/2024] Open
Abstract
Leaf rust (Puccinia triticina Eriks) is a wheat disease causing substantial yield losses in wheat production globally. The identification of genetic resources with permanently effective resistance genes and the generation of mutant lines showing increased levels of resistance allow the efficient incorporation of these target genes into germplasm pools by marker-assisted breeding. In this study, new mutant (M3 generation) lines generated from the rust-resistant variety Kazakhstanskaya-19 were developed using gamma-induced mutagenesis through 300-, 350-, and 400-Gy doses. In field trials after leaf rust inoculation, 75 mutant lines showed adult plant resistance. These lines were evaluated for resistance at the seedling stage via microscopy in greenhouse experiments. Most of these lines (89.33%) were characterized as resistant at both developmental stages. Hyperspectral imaging analysis indicated that infected leaves of wheat genotypes showed increased relative reflectance in visible and near-infrared light compared to the non-infected genotypes, with peak means at 462 and 644 nm, and 1936 and 2392 nm, respectively. Five spectral indexes, including red edge normalized difference vegetation index (RNDVI), structure-insensitive pigment index (SIPI), ratio vegetation index (RVSI), water index (WI), and normalized difference water index (NDWI), demonstrated significant potential for determining disease severity at the seedling stage. The most significant differences in reflectance between susceptible and resistant mutant lines appeared at 694.57 and 987.51 nm. The mutant lines developed were also used for the development and validation of KASP markers for leaf rust resistance genes Lr1, Lr2a, Lr3, Lr9, Lr10, and Lr17. The mutant lines had high frequencies of "a" resistance alleles (0.88) in all six Lr genes, which were significantly associated with seedling resistance and suggest the potential of favorable haplotype introgression through functional markers. Nine mutant lines characterized by the presence of "b" alleles in Lr9 and Lr10-except for one line with allele "a" in Lr9 and three mutant lines with allele "a" in Lr10-showed the progressive development of fungal haustorial mother cells 72 h after inoculation. One line from 300-Gy-dosed mutant germplasm with "b" alleles in Lr1, Lr2a, Lr10, and Lr17 and "a" alleles in Lr3 and Lr9 was characterized as resistant based on the low number of haustorial mother cells, suggesting the contribution of the "a" alleles of Lr3 and Lr9.
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Affiliation(s)
- Saule Kenzhebayeva
- Faculty of Biology and Biotechnology, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan; (S.S.); (S.A.); (N.O.); (G.D.); (S.A.); (A.A.)
| | - Shynarbek Mazkirat
- Kazakh Research Institute of Agriculture and Plant Growing, Almaty Region, Almalybak 040909, Kazakhstan; (S.M.); (A.A.)
| | - Sabina Shoinbekova
- Faculty of Biology and Biotechnology, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan; (S.S.); (S.A.); (N.O.); (G.D.); (S.A.); (A.A.)
| | - Saule Atabayeva
- Faculty of Biology and Biotechnology, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan; (S.S.); (S.A.); (N.O.); (G.D.); (S.A.); (A.A.)
| | - Alfia Abekova
- Kazakh Research Institute of Agriculture and Plant Growing, Almaty Region, Almalybak 040909, Kazakhstan; (S.M.); (A.A.)
| | - Nargul Omirbekova
- Faculty of Biology and Biotechnology, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan; (S.S.); (S.A.); (N.O.); (G.D.); (S.A.); (A.A.)
| | - Gulina Doktyrbay
- Faculty of Biology and Biotechnology, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan; (S.S.); (S.A.); (N.O.); (G.D.); (S.A.); (A.A.)
| | - Saltant Asrandina
- Faculty of Biology and Biotechnology, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan; (S.S.); (S.A.); (N.O.); (G.D.); (S.A.); (A.A.)
| | - Dinara Zharassova
- Mangyshlak Experimental Botanical Garden, Ministry of Science and Higher Education of the Republic of Kazakhstan, Aktau R00A3E0, Kazakhstan;
| | - Aigul Amirova
- Faculty of Biology and Biotechnology, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan; (S.S.); (S.A.); (N.O.); (G.D.); (S.A.); (A.A.)
| | - Albrecht Serfling
- Institute for Resistance Research and Stress Tolerance, Julius Kuehn-Institute (JKI) Federal Research Centre for Cultivated Plants, 06484 Quedlinburg, Germany;
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Honda S, Imamura A, Seki Y, Chigira K, Iwasa M, Hayami K, Nomura T, Ohkubo S, Ookawa T, Nagano AJ, Matsuoka M, Tanaka Y, Adachi S. Genome-wide association study of leaf photosynthesis using a high-throughput gas exchange system in rice. PHOTOSYNTHESIS RESEARCH 2024; 159:17-28. [PMID: 38112862 DOI: 10.1007/s11120-023-01065-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Accepted: 11/13/2023] [Indexed: 12/21/2023]
Abstract
Enhancing leaf photosynthetic capacity is essential for improving the yield of rice (Oryza sativa L.). Although the exploitation of natural genetic resources is considered a promising approach to enhance photosynthetic capacity, genomic factors related to the genetic diversity of leaf photosynthetic capacity have yet to be fully elucidated due to the limitation of measurement efficiency. In this study, we aimed to identify novel genomic regions for the net CO2 assimilation rate (A) by combining genome-wide association study (GWAS) and the newly developed rapid closed gas exchange system MIC-100. Using three MIC-100 systems in the field at the vegetative stage, we measured A of 168 temperate japonica rice varieties with six replicates for three years. We found that the modern varieties exhibited higher A than the landraces, while there was no significant relationship between the release year and A among the modern varieties. Our GWAS scan revealed two major peaks located on chromosomes 4 and 8, which were repeatedly detected in the different experiments and in the generalized linear modelling approach. We suggest that high-throughput gas exchange measurements combined with GWAS is a reliable approach for understanding the genetic mechanisms underlying photosynthetic diversities in crop species.
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Affiliation(s)
- Sotaro Honda
- Graduate School of Agriculture, Tokyo University of Agriculture and Technology, Tokyo, 183-8509, Japan
| | - Ayumu Imamura
- Graduate School of Agriculture, Ibaraki University, Ibaraki, 300-0393, Japan
| | - Yoshiaki Seki
- Graduate School of Agriculture, Tokyo University of Agriculture and Technology, Tokyo, 183-8509, Japan
| | - Koki Chigira
- Graduate School of Agriculture, Tokyo University of Agriculture and Technology, Tokyo, 183-8509, Japan
| | - Marina Iwasa
- Graduate School of Agriculture, Tokyo University of Agriculture and Technology, Tokyo, 183-8509, Japan
| | - Kentaro Hayami
- Graduate School of Agriculture, Tokyo University of Agriculture and Technology, Tokyo, 183-8509, Japan
| | - Tomohiro Nomura
- Graduate School of Agriculture, Tokyo University of Agriculture and Technology, Tokyo, 183-8509, Japan
| | - Satoshi Ohkubo
- Graduate School of Agriculture, Tokyo University of Agriculture and Technology, Tokyo, 183-8509, Japan
| | - Taiichiro Ookawa
- Graduate School of Agriculture, Tokyo University of Agriculture and Technology, Tokyo, 183-8509, Japan
| | - Atsushi J Nagano
- Faculty of Agriculture, Ryukoku University, Shiga, 520-2194, Japan
- Institute for Advanced Biosciences, Keio University, Yamagata, 997-0017, Japan
| | - Makoto Matsuoka
- Faculty of Food and Agricultural Sciences, Institute of Fermentation Sciences, Fukushima University, Fukushima, 960-1296, Japan
| | - Yu Tanaka
- Graduate School of Environment and Life Science, Okayama University, Okayama, 700-8530, Japan
| | - Shunsuke Adachi
- Graduate School of Agriculture, Tokyo University of Agriculture and Technology, Tokyo, 183-8509, Japan.
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4
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Stejskal J, Čepl J, Neuwirthová E, Akinyemi OO, Chuchlík J, Provazník D, Keinänen M, Campbell P, Albrechtová J, Lstibůrek M, Lhotáková Z. Making the Genotypic Variation Visible: Hyperspectral Phenotyping in Scots Pine Seedlings. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0111. [PMID: 38026471 PMCID: PMC10644830 DOI: 10.34133/plantphenomics.0111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 10/10/2023] [Indexed: 12/01/2023]
Abstract
Hyperspectral reflectance contains valuable information about leaf functional traits, which can indicate a plant's physiological status. Therefore, using hyperspectral reflectance for high-throughput phenotyping of foliar traits could be a powerful tool for tree breeders and nursery practitioners to distinguish and select seedlings with desired adaptation potential to local environments. We evaluated the use of 2 nondestructive methods (i.e., leaf and proximal/canopy) measuring hyperspectral reflectance in the 350- to 2,500-nm range for phenotyping on 1,788 individual Scots pine seedlings belonging to lowland and upland ecotypes of 3 different local populations from the Czech Republic. Leaf-level measurements were collected using a spectroradiometer and a contact probe with an internal light source to measure the biconical reflectance factor of a sample of needles placed on a black background in the contact probe field of view. The proximal canopy measurements were collected under natural solar light, using the same spectroradiometer with fiber optical cable to collect data on individual seedlings' hemispherical conical reflectance factor. The latter method was highly susceptible to changes in incoming radiation. Both spectral datasets showed statistically significant differences among Scots pine populations in the whole spectral range. Moreover, using random forest and support vector machine learning algorithms, the proximal data obtained from the top of the seedlings offered up to 83% accuracy in predicting 3 different Scots pine populations. We conclude that both approaches are viable for hyperspectral phenotyping to disentangle the phenotypic and the underlying genetic variation within Scots pine seedlings.
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Affiliation(s)
- Jan Stejskal
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences,
Czech University of Life Sciences Prague, Prague, Czech Republic
| | - Jaroslav Čepl
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences,
Czech University of Life Sciences Prague, Prague, Czech Republic
| | - Eva Neuwirthová
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences,
Czech University of Life Sciences Prague, Prague, Czech Republic
- Department of Experimental Plant Biology,
Charles University, Prague, Czech Republic
| | - Olusegun Olaitan Akinyemi
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences,
Czech University of Life Sciences Prague, Prague, Czech Republic
- Department of Environmental and Biological Sciences,
University of Eastern Finland, Joensuu, Finland
| | - Jiří Chuchlík
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences,
Czech University of Life Sciences Prague, Prague, Czech Republic
| | - Daniel Provazník
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences,
Czech University of Life Sciences Prague, Prague, Czech Republic
| | - Markku Keinänen
- Department of Environmental and Biological Sciences,
University of Eastern Finland, Joensuu, Finland
- Center for Photonic Sciences,
University of Eastern Finland, Joensuu, Finland
| | - Petya Campbell
- Department of Geography and Environmental Sciences,
University of Maryland Baltimore County, Baltimore, MD, USA
- Biospheric Sciences Laboratory,
NASA Goddard Space Flight Center, Greenbelt, MD, USA
| | - Jana Albrechtová
- Department of Experimental Plant Biology,
Charles University, Prague, Czech Republic
| | - Milan Lstibůrek
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences,
Czech University of Life Sciences Prague, Prague, Czech Republic
| | - Zuzana Lhotáková
- Department of Experimental Plant Biology,
Charles University, Prague, Czech Republic
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5
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Miraglio T, Coops NC, Wallis CIB, Crofts AL, Kalacska M, Vellend M, Serbin SP, Arroyo-Mora JP, Laliberté E. Mapping canopy traits over Québec using airborne and spaceborne imaging spectroscopy. Sci Rep 2023; 13:17179. [PMID: 37821515 PMCID: PMC10567784 DOI: 10.1038/s41598-023-44384-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 10/07/2023] [Indexed: 10/13/2023] Open
Abstract
The advent of new spaceborne imaging spectrometers offers new opportunities for ecologists to map vegetation traits at global scales. However, to date most imaging spectroscopy studies exploiting satellite spectrometers have been constrained to the landscape scale. In this paper we present a new method to map vegetation traits at the landscape scale and upscale trait maps to the continental level, using historical spaceborne imaging spectroscopy (Hyperion) to derive estimates of leaf mass per area, nitrogen, and carbon concentrations of forests in Québec, Canada. We compare estimates for each species with reference field values and obtain good agreement both at the landscape and continental scales, with patterns consistent with the leaf economic spectrum. By exploiting the Hyperion satellite archive to map these traits and successfully upscale the estimates to the continental scale, we demonstrate the great potential of recent and upcoming spaceborne spectrometers to benefit plant biodiversity monitoring and conservation efforts.
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Affiliation(s)
- Thomas Miraglio
- Integrated Remote Sensing Studio, Department of Forest Resources Management, University of British Columbia, 2424 Main Mall, Vancouver, BC, V6T 1Z4, Canada.
| | - Nicholas C Coops
- Integrated Remote Sensing Studio, Department of Forest Resources Management, University of British Columbia, 2424 Main Mall, Vancouver, BC, V6T 1Z4, Canada
| | | | - Anna L Crofts
- Département de Biologie, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Margaret Kalacska
- Applied Remote Sensing Lab, Department of Geography, McGill University, Montréal, QC, H3A 0G4, Canada
| | - Mark Vellend
- Département de Biologie, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Shawn P Serbin
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, 11973, USA
| | - Juan Pablo Arroyo-Mora
- Flight Research Laboratory, National Research Council of Canada, Ottawa, ON, K1A 0R6, Canada
| | - Etienne Laliberté
- Département de Sciences Biologiques, Institut de Recherche en Biologie Végétale, Université de Montréal, Montréal, QC, H3A 0G4, Canada
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6
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Varghese R, Cherukuri AK, Doddrell NH, Doss CGP, Simkin AJ, Ramamoorthy S. Machine learning in photosynthesis: Prospects on sustainable crop development. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2023; 335:111795. [PMID: 37473784 DOI: 10.1016/j.plantsci.2023.111795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 07/10/2023] [Accepted: 07/13/2023] [Indexed: 07/22/2023]
Abstract
Improving photosynthesis is a promising avenue to increase food security. Studying photosynthetic traits with the aim to improve efficiency has been one of many strategies to increase crop yield but analyzing large data sets presents an ongoing challenge. Machine learning (ML) represents a ubiquitous tool that can provide a more elaborate data analysis. Here we review the application of ML in various domains of photosynthetic research, as well as in photosynthetic pigment studies. We highlight how correlating hyperspectral data with photosynthetic parameters to improve crop yield could be achieved through various ML algorithms. We also propose strategies to employ ML in promoting photosynthetic pigment research for furthering crop yield.
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Affiliation(s)
- Ressin Varghese
- School of Bio Sciences and Technology, VIT University, Vellore 632014, Tamil Nadu, India
| | - Aswani Kumar Cherukuri
- School of Information Technology and Engineering, VIT University, Vellore 632014, Tamil Nadu, India
| | | | - C George Priya Doss
- School of Bio Sciences and Technology, VIT University, Vellore 632014, Tamil Nadu, India
| | - Andrew J Simkin
- School of Biosciences, University of Kent, Canterbury CT2 7NJ, UK; School of Life Sciences, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK
| | - Siva Ramamoorthy
- School of Bio Sciences and Technology, VIT University, Vellore 632014, Tamil Nadu, India.
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7
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Kim SH, Subramanian P, Na YW, Hahn BS, Kim Y. RDA-Genebank and Digital Phenotyping for Next-Generation Research on Plant Genetic Resources. PLANTS (BASEL, SWITZERLAND) 2023; 12:2825. [PMID: 37570979 PMCID: PMC10421229 DOI: 10.3390/plants12152825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 07/24/2023] [Accepted: 07/28/2023] [Indexed: 08/13/2023]
Abstract
The National Agrobiodiversity Center under the Rural Development Administration (RDA) in Jeonju, Republic of Korea stands as the foremost national genebank in the country. Over the years, the National Agrobiodiversity Center has remained committed to enriching its collection with foreign genetic resources, elevating its status to a world-class plant genetic resources (PGR)- holding genebank. Currently, several steps are being undertaken to improve the accessibility of the collection to national as well as international researchers, improve the data available on the resources, and amend the passport information for the accessions. With the implementation of the Nagoya Protocol, the origin of genetic resources is being highlighted as an important input in the passport information. The RDA-Genebank actively responds to the Nagoya Protocol by supplementing passport data for resources lacking information on their origin. In addition, a large number of conserved resources are continuously multiplied, and agronomic traits are investigated concurrently. With the traditional methods of characterization of the germplasm requiring a significant amount of time and effort, we have initiated high-throughput phenotyping using digital techniques to improve our germplasm data. Primarily, we have started adding seed phenotype information followed by measuring root phenotypes which are stored under agronomic traits. This may be the initial step toward using largescale high-throughput techniques for a germplasm. In this study, we aim to provide an introduction to the RDA-Genebank, to adopted international standards, and to the establishment of high-throughput phenotyping techniques for the improvement of passport information.
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Affiliation(s)
- Seong-Hoon Kim
- National Agrobiodiversity Center, National Institute of Agricultural Sciences, RDA, Jeonju 5487, Republic of Korea (Y.-W.N.); (B.-S.H.)
| | - Parthiban Subramanian
- National Agrobiodiversity Center, National Institute of Agricultural Sciences, RDA, Jeonju 5487, Republic of Korea (Y.-W.N.); (B.-S.H.)
| | - Young-Wang Na
- National Agrobiodiversity Center, National Institute of Agricultural Sciences, RDA, Jeonju 5487, Republic of Korea (Y.-W.N.); (B.-S.H.)
| | - Bum-Soo Hahn
- National Agrobiodiversity Center, National Institute of Agricultural Sciences, RDA, Jeonju 5487, Republic of Korea (Y.-W.N.); (B.-S.H.)
| | - Yoonha Kim
- Laboratory of Crop Production, Department of Applied Biosciences, Kyungpook National University, Daegu 41566, Republic of Korea
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Wong CYS. Plant optics: underlying mechanisms in remotely sensed signals for phenotyping applications. AOB PLANTS 2023; 15:plad039. [PMID: 37560760 PMCID: PMC10407989 DOI: 10.1093/aobpla/plad039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 07/04/2023] [Indexed: 08/11/2023]
Abstract
Optical-based remote sensing offers great potential for phenotyping vegetation traits and functions for a range of applications including vegetation monitoring and assessment. A key strength of optical-based approaches is the underlying mechanistic link to vegetation physiology, biochemistry, and structure that influences a spectral signal. By exploiting spectral variation driven by plant physiological response to environment, remotely sensed products can be used to estimate vegetation traits and functions. However, oftentimes these products are proxies based on covariance, which can lead to misinterpretation and decoupling under certain scenarios. This viewpoint will discuss (i) the optical properties of vegetation, (ii) applications of vegetation indices, solar-induced fluorescence, and machine-learning approaches, and (iii) how covariance can lead to good empirical proximation of plant traits and functions. Understanding and acknowledging the underlying mechanistic basis of plant optics must be considered as remotely sensed data availability and applications continue to grow. Doing so will enable appropriate application and consideration of limitations for the use of optical-based remote sensing for phenotyping applications.
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9
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Dinish US, Teng MTJ, Xinhui VT, Dev K, Tan JJ, Koh SS, Urano D, Olivo M. Miniaturized Vis-NIR handheld spectrometer for non-invasive pigment quantification in agritech applications. Sci Rep 2023; 13:9524. [PMID: 37308523 DOI: 10.1038/s41598-023-36220-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 05/31/2023] [Indexed: 06/14/2023] Open
Abstract
Advanced precision agriculture requires the objective measurement of the structural and functional properties of plants. Biochemical profiles in leaves can differ depending on plant growing conditions. By quantitatively detecting these changes, farm production processes can be optimized to achieve high-yield, high-quality, and nutrient dense agricultural products. To enable the rapid and non-destructive detection on site, this study demonstrates the development of a new custom-designed portable handheld Vis-NIR spectrometer that collects leaf reflectance spectra, wirelessly transfers the spectral data through Bluetooth, and provides both raw spectral data and processed information. The spectrometer has two preprogramed methods: anthocyanin and chlorophyll quantification. Anthocyanin content of red and green lettuce estimated with the new spectrometer showed an excellent correlation coefficient of 0.84 with those determined by a destructive gold standard biochemical method. The differences in chlorophyll content were measured using leaf senescence as a case study. Chlorophyll Index calculated with the handheld spectrometer gradually decreased with leaf age as chlorophyll degrades during the process of senescence. The estimated chlorophyll values were highly correlated with those obtained from a commercial fluorescence-based chlorophyll meter with a correlation coefficient of 0.77. The developed portable handheld Vis-NIR spectrometer could be a simple, cost-effective, and easy to operate tool that can be used to non-invasively monitor plant pigment and nutrient content efficiently.
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Affiliation(s)
- U S Dinish
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis #08-03, Singapore, 138634, Singapore.
| | - Mark Teo Ju Teng
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis #08-03, Singapore, 138634, Singapore
| | - Valerie Teo Xinhui
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis #08-03, Singapore, 138634, Singapore
| | - Kapil Dev
- Institute of Bioengineering and Bioimaging, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Javier Jingheng Tan
- Temasek Life Sciences Laboratory, National University of Singapore, Singapore, Singapore
| | - Sally Shuxian Koh
- Temasek Life Sciences Laboratory, National University of Singapore, Singapore, Singapore
- Department of Biological Sciences, National University of Singapore, Singapore, Singapore
| | - Daisuke Urano
- Temasek Life Sciences Laboratory, National University of Singapore, Singapore, Singapore.
- Department of Biological Sciences, National University of Singapore, Singapore, Singapore.
| | - Malini Olivo
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis #08-03, Singapore, 138634, Singapore.
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10
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Wijewardane NK, Zhang H, Yang J, Schnable JC, Schachtman DP, Ge Y. A leaf-level spectral library to support high throughput plant phenotyping: Predictive accuracy and model transfer. JOURNAL OF EXPERIMENTAL BOTANY 2023:erad129. [PMID: 37018460 PMCID: PMC10400152 DOI: 10.1093/jxb/erad129] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Indexed: 06/19/2023]
Abstract
Leaf-level hyperspectral reflectance has become an effective tool for high-throughput phenotyping of plant leaf traits due to its rapid, low-cost, multi-sensing, and non-destructive nature. However, collecting samples for model calibration can still be expensive; and models show poor transferability among different datasets. This study had three specific objectives: (i) assemble a large library of leaf hyperspectral data (n=2460) from maize and sorghum, (ii) evaluate two machine-learning approaches to estimate nine leaf properties (chlorophyll, thickness, water content, nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur), and (iii) investigate the usefulness of this spectral library for predicting external datasets (n=445) including soybean and camelina using extra-weighted spiking. Internal cross-validation showed satisfactory performance of the spectral library to estimate all nine traits (average R 2 0.688), with Partial Least Squares Regression outperforming Deep Neural Network models. Models calibrated solely using the spectral library showed degraded performance on external datasets (average R 2 0.159 for camelina, 0.337 for soybean). Models improved significantly when a small portion of external samples (n=20) was added to the library via extra-weighted spiking (average R 2 0.574 for camelina, 0.536 for soybean). The leaf-level spectral library greatly benefits plant physiological and biochemical phenotyping; whereas extra-weight spiking improves model transferability and extends its utility.
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Affiliation(s)
- Nuwan K Wijewardane
- Department of Agricultural and Biological Engineering, Mississippi State University, Starkville, MS, USA
| | - Huichun Zhang
- College of Mechanical and Electrical Engineering, Nanjing Forestry University, China
- Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing, China
| | - Jinliang Yang
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - James C Schnable
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Daniel P Schachtman
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Yufeng Ge
- Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE, USA
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11
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O’Leary BM, Scafaro AP, York LM. High-throughput, dynamic, multi-dimensional: an expanding repertoire of plant respiration measurements. PLANT PHYSIOLOGY 2023; 191:2070-2083. [PMID: 36638140 PMCID: PMC10069890 DOI: 10.1093/plphys/kiac580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 11/30/2022] [Indexed: 06/17/2023]
Abstract
A recent burst of technological innovation and adaptation has greatly improved our ability to capture respiration rate data from plant sources. At the tissue level, several independent respiration measurement options are now available, each with distinct advantages and suitability, including high-throughput sampling capacity. These advancements facilitate the inclusion of respiration rate data into large-scale biological studies such as genetic screens, ecological surveys, crop breeding trials, and multi-omics molecular studies. As a result, our understanding of the correlations of respiration with other biological and biochemical measurements is rapidly increasing. Difficult questions persist concerning the interpretation and utilization of respiration data; concepts such as allocation of respiration to growth versus maintenance, the unnecessary or inefficient use of carbon and energy by respiration, and predictions of future respiration rates in response to environmental change are all insufficiently grounded in empirical data. However, we emphasize that new experimental designs involving novel combinations of respiration rate data with other measurements will flesh-out our current theories of respiration. Furthermore, dynamic recordings of respiration rate, which have long been used at the scale of mitochondria, are increasingly being used at larger scales of size and time to reflect processes of cellular signal transduction and physiological response to the environment. We also highlight how respiratory methods are being better adapted to different plant tissues including roots and seeds, which have been somewhat neglected historically.
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Affiliation(s)
- Brendan M O’Leary
- Saskatoon Research and Development Centre, Agriculture and Agri-food Canada, Saskatoon S7N 0X2, Canada
| | - Andrew P Scafaro
- Research School of Biology, Australian National University, Canberra, ACT 2601, Australia
| | - Larry M York
- Center for Bioenergy Innovation and Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
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12
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Wong CYS, Jones T, McHugh DP, Gilbert ME, Gepts P, Palkovic A, Buckley TN, Magney TS. TSWIFT: Tower Spectrometer on Wheels for Investigating Frequent Timeseries for high-throughput phenotyping of vegetation physiology. PLANT METHODS 2023; 19:29. [PMID: 36978119 PMCID: PMC10044391 DOI: 10.1186/s13007-023-01001-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 02/24/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Remote sensing instruments enable high-throughput phenotyping of plant traits and stress resilience across scale. Spatial (handheld devices, towers, drones, airborne, and satellites) and temporal (continuous or intermittent) tradeoffs can enable or constrain plant science applications. Here, we describe the technical details of TSWIFT (Tower Spectrometer on Wheels for Investigating Frequent Timeseries), a mobile tower-based hyperspectral remote sensing system for continuous monitoring of spectral reflectance across visible-near infrared regions with the capacity to resolve solar-induced fluorescence (SIF). RESULTS We demonstrate potential applications for monitoring short-term (diurnal) and long-term (seasonal) variation of vegetation for high-throughput phenotyping applications. We deployed TSWIFT in a field experiment of 300 common bean genotypes in two treatments: control (irrigated) and drought (terminal drought). We evaluated the normalized difference vegetation index (NDVI), photochemical reflectance index (PRI), and SIF, as well as the coefficient of variation (CV) across the visible-near infrared spectral range (400 to 900 nm). NDVI tracked structural variation early in the growing season, following initial plant growth and development. PRI and SIF were more dynamic, exhibiting variation diurnally and seasonally, enabling quantification of genotypic variation in physiological response to drought conditions. Beyond vegetation indices, CV of hyperspectral reflectance showed the most variability across genotypes, treatment, and time in the visible and red-edge spectral regions. CONCLUSIONS TSWIFT enables continuous and automated monitoring of hyperspectral reflectance for assessing variation in plant structure and function at high spatial and temporal resolutions for high-throughput phenotyping. Mobile, tower-based systems like this can provide short- and long-term datasets to assess genotypic and/or management responses to the environment, and ultimately enable the spectral prediction of resource-use efficiency, stress resilience, productivity and yield.
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Affiliation(s)
| | - Taylor Jones
- Department of Earth & Environment, Boston University, Boston, MA 02215 USA
| | - Devin P. McHugh
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616 USA
| | - Matthew E. Gilbert
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616 USA
| | - Paul Gepts
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616 USA
| | - Antonia Palkovic
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616 USA
| | - Thomas N. Buckley
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616 USA
| | - Troy S. Magney
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616 USA
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13
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Wong CYS, Gilbert ME, Pierce MA, Parker TA, Palkovic A, Gepts P, Magney TS, Buckley TN. Hyperspectral Remote Sensing for Phenotyping the Physiological Drought Response of Common and Tepary Bean. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0021. [PMID: 37040284 PMCID: PMC10076057 DOI: 10.34133/plantphenomics.0021] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 12/12/2022] [Indexed: 06/19/2023]
Abstract
Proximal remote sensing offers a powerful tool for high-throughput phenotyping of plants for assessing stress response. Bean plants, an important legume for human consumption, are often grown in regions with limited rainfall and irrigation and are therefore bred to further enhance drought tolerance. We assessed physiological (stomatal conductance and predawn and midday leaf water potential) and ground- and tower-based hyperspectral remote sensing (400 to 2,400 nm and 400 to 900 nm, respectively) measurements to evaluate drought response in 12 common bean and 4 tepary bean genotypes across 3 field campaigns (1 predrought and 2 post-drought). Hyperspectral data in partial least squares regression models predicted these physiological traits (R 2 = 0.20 to 0.55; root mean square percent error 16% to 31%). Furthermore, ground-based partial least squares regression models successfully ranked genotypic drought responses similar to the physiologically based ranks. This study demonstrates applications of high-resolution hyperspectral remote sensing for predicting plant traits and phenotyping drought response across genotypes for vegetation monitoring and breeding population screening.
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14
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Che S, Du G, Zhong X, Mo Z, Wang Z, Mao Y. Quantification of Photosynthetic Pigments in Neopyropia yezoensis Using Hyperspectral Imagery. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0012. [PMID: 37040513 PMCID: PMC10076050 DOI: 10.34133/plantphenomics.0012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 11/17/2022] [Indexed: 06/19/2023]
Abstract
Phycobilisomes and chlorophyll-a (Chla) play important roles in the photosynthetic physiology of red macroalgae and serve as the primary light-harvesting antennae and reaction center for photosystem II. Neopyropia is an economically important red macroalga widely cultivated in East Asian countries. The contents and ratios of 3 main phycobiliproteins and Chla are visible traits to evaluate its commercial quality. The traditional analytical methods used for measuring these components have several limitations. Therefore, a high-throughput, nondestructive, optical method based on hyperspectral imaging technology was developed for phenotyping the pigments phycoerythrin (PE), phycocyanin (PC), allophycocyanin (APC), and Chla in Neopyropia thalli in this study. The average spectra from the region of interest were collected at wavelengths ranging from 400 to 1000 nm using a hyperspectral camera. Following different preprocessing methods, 2 machine learning methods, partial least squares regression (PLSR) and support vector machine regression (SVR), were performed to establish the best prediction models for PE, PC, APC, and Chla contents. The prediction results showed that the PLSR model performed the best for PE (R Test 2 = 0.96, MAPE = 8.31%, RPD = 5.21) and the SVR model performed the best for PC (R Test 2 = 0.94, MAPE = 7.18%, RPD = 4.16) and APC (R Test 2 = 0.84, MAPE = 18.25%, RPD = 2.53). Two models (PLSR and SVR) performed almost the same for Chla (PLSR: R Test 2 = 0.92, MAPE = 12.77%, RPD = 3.61; SVR: R Test 2 = 0.93, MAPE = 13.51%, RPD =3.60). Further validation of the optimal models was performed using field-collected samples, and the result demonstrated satisfactory robustness and accuracy. The distribution of PE, PC, APC, and Chla contents within a thallus was visualized according to the optimal prediction models. The results showed that hyperspectral imaging technology was effective for fast, accurate, and noninvasive phenotyping of the PE, PC, APC, and Chla contents of Neopyropia in situ. This could benefit the efficiency of macroalgae breeding, phenomics research, and other related applications.
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Affiliation(s)
- Shuai Che
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003, China
| | - Guoying Du
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003, China
| | - Xuefeng Zhong
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003, China
| | - Zhaolan Mo
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003, China
| | - Zhendong Wang
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003, China
| | - Yunxiang Mao
- Key Laboratory of Utilization and Conservation of Tropical Marine Bioresource (Ministry of Education), College of Fisheries and Life Science, Hainan Tropical Ocean University, Sanya, 572002, China
- Yazhou Bay Innovation Institute, Hainan Tropical Ocean University, Sanya, 572025, China
- Laboratory for Marine Biology and Biotechnology, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao, 266073, China
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15
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Murchie EH, Reynolds M, Slafer GA, Foulkes MJ, Acevedo-Siaca L, McAusland L, Sharwood R, Griffiths S, Flavell RB, Gwyn J, Sawkins M, Carmo-Silva E. A 'wiring diagram' for source strength traits impacting wheat yield potential. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:72-90. [PMID: 36264277 PMCID: PMC9786870 DOI: 10.1093/jxb/erac415] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 10/18/2022] [Indexed: 05/06/2023]
Abstract
Source traits are currently of great interest for the enhancement of yield potential; for example, much effort is being expended to find ways of modifying photosynthesis. However, photosynthesis is but one component of crop regulation, so sink activities and the coordination of diverse processes throughout the crop must be considered in an integrated, systems approach. A set of 'wiring diagrams' has been devised as a visual tool to integrate the interactions of component processes at different stages of wheat development. They enable the roles of chloroplast, leaf, and whole-canopy processes to be seen in the context of sink development and crop growth as a whole. In this review, we dissect source traits both anatomically (foliar and non-foliar) and temporally (pre- and post-anthesis), and consider the evidence for their regulation at local and whole-plant/crop levels. We consider how the formation of a canopy creates challenges (self-occlusion) and opportunities (dynamic photosynthesis) for components of photosynthesis. Lastly, we discuss the regulation of source activity by feedback regulation. The review is written in the framework of the wiring diagrams which, as integrated descriptors of traits underpinning grain yield, are designed to provide a potential workspace for breeders and other crop scientists that, along with high-throughput and precision phenotyping data, genetics, and bioinformatics, will help build future dynamic models of trait and gene interactions to achieve yield gains in wheat and other field crops.
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Affiliation(s)
| | - Matthew Reynolds
- International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera Mexico-Veracruz, El Batan, Texcoco, Mexico
| | - Gustavo A Slafer
- Department of Crop and Forest Sciences, University of Lleida–AGROTECNIO-CERCA Center, Av. R. Roure 191, 25198 Lleida, Spain
- ICREA (Catalonian Institution for Research and Advanced Studies), Barcelona, Spain
| | - M John Foulkes
- Division of Plant and Crop Science, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, UK
| | - Liana Acevedo-Siaca
- International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera Mexico-Veracruz, El Batan, Texcoco, Mexico
| | - Lorna McAusland
- Division of Plant and Crop Science, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, UK
| | - Robert Sharwood
- Hawkesbury Institute for the Environment, Western Sydney University, Richmond NSW 2753, Australia
| | - Simon Griffiths
- John Innes Centre, Norwich Research Park, Colney Ln, Norwich NR4 7UH, UK
| | - Richard B Flavell
- International Wheat Yield Partnership, 1500 Research Parkway, College Station, TX 77843, USA
| | - Jeff Gwyn
- International Wheat Yield Partnership, 1500 Research Parkway, College Station, TX 77843, USA
| | - Mark Sawkins
- International Wheat Yield Partnership, 1500 Research Parkway, College Station, TX 77843, USA
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16
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Garcia A, Gaju O, Bowerman AF, Buck SA, Evans JR, Furbank RT, Gilliham M, Millar AH, Pogson BJ, Reynolds MP, Ruan Y, Taylor NL, Tyerman SD, Atkin OK. Enhancing crop yields through improvements in the efficiency of photosynthesis and respiration. THE NEW PHYTOLOGIST 2023; 237:60-77. [PMID: 36251512 PMCID: PMC10100352 DOI: 10.1111/nph.18545] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 09/15/2022] [Indexed: 06/06/2023]
Abstract
The rate with which crop yields per hectare increase each year is plateauing at the same time that human population growth and other factors increase food demand. Increasing yield potential (Y p ) of crops is vital to address these challenges. In this review, we explore a component ofY p that has yet to be optimised - that being improvements in the efficiency with which light energy is converted into biomass (ε c ) via modifications to CO2 fixed per unit quantum of light (α), efficiency of respiratory ATP production (ε prod ) and efficiency of ATP use (ε use ). For α, targets include changes in photoprotective machinery, ribulose bisphosphate carboxylase/oxygenase kinetics and photorespiratory pathways. There is also potential forε prod to be increased via targeted changes to the expression of the alternative oxidase and mitochondrial uncoupling pathways. Similarly, there are possibilities to improveε use via changes to the ATP costs of phloem loading, nutrient uptake, futile cycles and/or protein/membrane turnover. Recently developed high-throughput measurements of respiration can serve as a proxy for the cumulative energy cost of these processes. There are thus exciting opportunities to use our growing knowledge of factors influencing the efficiency of photosynthesis and respiration to create a step-change in yield potential of globally important crops.
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Affiliation(s)
- Andres Garcia
- ARC Centre of Excellence in Plant Energy Biology, Research School of BiologyThe Australian National UniversityCanberraACT2601Australia
- Division of Plant Sciences, Research School of BiologyAustralian National UniversityCanberraACT2601Australia
| | - Oorbessy Gaju
- ARC Centre of Excellence in Plant Energy Biology, Research School of BiologyThe Australian National UniversityCanberraACT2601Australia
- College of Science, Lincoln Institute for Agri‐Food TechnologyUniversity of LincolnLincolnshireLN2 2LGUK
| | - Andrew F. Bowerman
- ARC Centre of Excellence in Plant Energy Biology, Research School of BiologyThe Australian National UniversityCanberraACT2601Australia
- Division of Plant Sciences, Research School of BiologyAustralian National UniversityCanberraACT2601Australia
| | - Sally A. Buck
- ARC Centre of Excellence in Plant Energy Biology, Research School of BiologyThe Australian National UniversityCanberraACT2601Australia
- Division of Plant Sciences, Research School of BiologyAustralian National UniversityCanberraACT2601Australia
| | - John R. Evans
- Division of Plant Sciences, Research School of BiologyAustralian National UniversityCanberraACT2601Australia
- ARC Centre of Excellence for Translational Photosynthesis, Research School of BiologyThe Australian National UniversityCanberraACT2601Australia
| | - Robert T. Furbank
- Division of Plant Sciences, Research School of BiologyAustralian National UniversityCanberraACT2601Australia
- ARC Centre of Excellence for Translational Photosynthesis, Research School of BiologyThe Australian National UniversityCanberraACT2601Australia
| | - Matthew Gilliham
- ARC Centre of Excellence in Plant Energy Biology, School of Agriculture, Food and Wine & Waite Research InstituteUniversity of AdelaideGlen OsmondSA5064Australia
| | - A. Harvey Millar
- ARC Centre of Excellence in Plant Energy Biology, School of Molecular Sciences & Institute of AgricultureThe University of Western AustraliaCrawleyWA6009Australia
| | - Barry J. Pogson
- ARC Centre of Excellence in Plant Energy Biology, Research School of BiologyThe Australian National UniversityCanberraACT2601Australia
- Division of Plant Sciences, Research School of BiologyAustralian National UniversityCanberraACT2601Australia
| | - Matthew P. Reynolds
- International Maize and Wheat Improvement Center (CIMMYT)Km. 45, Carretera Mexico, El BatanTexcoco56237Mexico
| | - Yong‐Ling Ruan
- Division of Plant Sciences, Research School of BiologyAustralian National UniversityCanberraACT2601Australia
| | - Nicolas L. Taylor
- ARC Centre of Excellence in Plant Energy Biology, School of Molecular Sciences & Institute of AgricultureThe University of Western AustraliaCrawleyWA6009Australia
| | - Stephen D. Tyerman
- ARC Centre of Excellence in Plant Energy Biology, School of Agriculture, Food and Wine & Waite Research InstituteUniversity of AdelaideGlen OsmondSA5064Australia
| | - Owen K. Atkin
- ARC Centre of Excellence in Plant Energy Biology, Research School of BiologyThe Australian National UniversityCanberraACT2601Australia
- Division of Plant Sciences, Research School of BiologyAustralian National UniversityCanberraACT2601Australia
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17
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Song G, Wang Q, Jin J. Temporal instability of partial least squares regressions for estimating leaf photosynthetic traits from hyperspectral information. JOURNAL OF PLANT PHYSIOLOGY 2022; 279:153831. [PMID: 36252398 DOI: 10.1016/j.jplph.2022.153831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 09/09/2022] [Accepted: 09/25/2022] [Indexed: 06/16/2023]
Abstract
Partial least squares regression (PLSR) is applied increasingly often to predict plant photosynthesis from reflectance spectra. While its applicability across different areas has been examined in previous studies, its stability across time has yet to be evaluated. In this study, we assessed a series of PLSR models built upon three different band selection approaches (iterative stepwise, genetic algorithm, and uninformative variable elimination), in combination with different spectral transforms (original and first-order derivative spectra), for their stabilities in predicting the maximum carboxylation rate (Vcmax) and maximum electron transport rate (Jmax) from hyperspectral reflectance spectra at different temporal scales (seasonal and interannual). The results showed that both photosynthetic parameters can be estimated from leaf hyperspectral reflectance with moderate to good accuracy across different growing stages (R2 = 0.45-0.84) and years (R2 = 0.37-0.97). We further found that the iterative stepwise selection of informative bands when building PLSR models could greatly improve its predictive capacity compared with that of other PLSR models, especially those based on first-order derivative spectra. However, the selected bands of the models for both photosynthetic parameters were, unfortunately not consistent. Furthermore, we could not have identified any model with fixed spectra performed consistently across different seasonal stages and across different years. However, the blue spectral regions were popularly selected throughout the growing stages and in different years. The results demonstrate that leaf spectra-trait estimation using PLSR models varies with time and thus cast doubt over the use of a specific PLSR model to infer leaf traits across different temporal-spatial contexts. The development of a general applicable PLSR model is still in the works.
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Affiliation(s)
- Guangman Song
- Graduate School of Science and Technology, Shizuoka University, Shizuoka, 422-8529, Japan
| | - Quan Wang
- Faculty of Agriculture, Shizuoka University, Shizuoka, 422-8529, Japan.
| | - Jia Jin
- Faculty of Agriculture, Shizuoka University, Shizuoka, 422-8529, Japan
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18
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Shu M, Zhou L, Chen H, Wang X, Meng L, Ma Y. Estimation of amino acid contents in maize leaves based on hyperspectral imaging. FRONTIERS IN PLANT SCIENCE 2022; 13:885794. [PMID: 35991404 PMCID: PMC9381814 DOI: 10.3389/fpls.2022.885794] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 07/11/2022] [Indexed: 06/15/2023]
Abstract
Estimation of the amino acid content in maize leaves is helpful for improving maize yield estimation and nitrogen use efficiency. Hyperspectral imaging can be used to obtain the physiological and biochemical parameters of maize leaves with the advantages of being rapid, non-destructive, and high throughput. This study aims to estimate the multiple amino acid contents in maize leaves using hyperspectral imaging data. Two nitrogen (N) fertilizer experiments were carried out to obtain the hyperspectral images of fresh maize leaves. The partial least squares regression (PLSR) method was used to build the estimation models of various amino acid contents by using the reflectance of all bands, sensitive band range, and sensitive bands. The models were then validated with the independent dataset. The results showed that (1) the spectral reflectance of most amino acids was more sensitive in the range of 400-717.08 nm than other bands. The estimation accuracy was better by using the reflectance of the sensitive band range than that of all bands; (2) the sensitive bands of most amino acids were in the ranges of 505.39-605 nm and 651-714 nm; and (3) among the 24 amino acids, the estimation models of the β-aminobutyric acid, ornithine, citrulline, methionine, and histidine achieved higher accuracy than those of other amino acids, with the R 2, relative root mean square error (RE), and relative percent deviation (RPD) of the measured and estimated value of testing samples in the range of 0.84-0.96, 8.79%-19.77%, and 2.58-5.18, respectively. This study can provide a non-destructive and rapid diagnostic method for genetic sensitive analysis and variety improvement of maize.
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Affiliation(s)
- Meiyan Shu
- College of Land Science and Technology, China Agricultural University, Beijing, China
| | - Long Zhou
- College of Biological Science, China Agricultural University, Beijing, China
| | - Haochong Chen
- College of Land Science and Technology, China Agricultural University, Beijing, China
| | - Xiqing Wang
- College of Biological Science, China Agricultural University, Beijing, China
| | - Lei Meng
- Department of Geography, Environment, and Tourism, Western Michigan University, Kalamazoo, MI, United States
| | - Yuntao Ma
- College of Land Science and Technology, China Agricultural University, Beijing, China
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19
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Montes CM, Fox C, Sanz-Sáez Á, Serbin SP, Kumagai E, Krause MD, Xavier A, Specht JE, Beavis WD, Bernacchi CJ, Diers BW, Ainsworth EA. High-throughput characterization, correlation, and mapping of leaf photosynthetic and functional traits in the soybean (Glycine max) nested association mapping population. Genetics 2022; 221:iyac065. [PMID: 35451475 PMCID: PMC9157091 DOI: 10.1093/genetics/iyac065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 04/03/2022] [Indexed: 11/14/2022] Open
Abstract
Photosynthesis is a key target to improve crop production in many species including soybean [Glycine max (L.) Merr.]. A challenge is that phenotyping photosynthetic traits by traditional approaches is slow and destructive. There is proof-of-concept for leaf hyperspectral reflectance as a rapid method to model photosynthetic traits. However, the crucial step of demonstrating that hyperspectral approaches can be used to advance understanding of the genetic architecture of photosynthetic traits is untested. To address this challenge, we used full-range (500-2,400 nm) leaf reflectance spectroscopy to build partial least squares regression models to estimate leaf traits, including the rate-limiting processes of photosynthesis, maximum Rubisco carboxylation rate, and maximum electron transport. In total, 11 models were produced from a diverse population of soybean sampled over multiple field seasons to estimate photosynthetic parameters, chlorophyll content, leaf carbon and leaf nitrogen percentage, and specific leaf area (with R2 from 0.56 to 0.96 and root mean square error approximately <10% of the range of calibration data). We explore the utility of these models by applying them to the soybean nested association mapping population, which showed variability in photosynthetic and leaf traits. Genetic mapping provided insights into the underlying genetic architecture of photosynthetic traits and potential improvement in soybean. Notably, the maximum Rubisco carboxylation rate mapped to a region of chromosome 19 containing genes encoding multiple small subunits of Rubisco. We also mapped the maximum electron transport rate to a region of chromosome 10 containing a fructose 1,6-bisphosphatase gene, encoding an important enzyme in the regeneration of ribulose 1,5-bisphosphate and the sucrose biosynthetic pathway. The estimated rate-limiting steps of photosynthesis were low or negatively correlated with yield suggesting that these traits are not influenced by the same genetic mechanisms and are not limiting yield in the soybean NAM population. Leaf carbon percentage, leaf nitrogen percentage, and specific leaf area showed strong correlations with yield and may be of interest in breeding programs as a proxy for yield. This work is among the first to use hyperspectral reflectance to model and map the genetic architecture of the rate-limiting steps of photosynthesis.
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Affiliation(s)
| | - Carolyn Fox
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Álvaro Sanz-Sáez
- Department of Crop, Soil, and Environmental Sciences, Auburn, AL 36849, USA
| | - Shawn P Serbin
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Etsushi Kumagai
- Institute of Agro-environmental Sciences, National Agriculture and Food Research Organization, Tsukuba, Ibaraki 305-8604, Japan
| | - Matheus D Krause
- Department of Agronomy, Iowa State University, Agronomy Hall, Ames, IA 50011, USA
| | - Alencar Xavier
- Department of Agronomy, Purdue University, West Lafayette, IN 47907, USA
- Department of Biostatistics, Corteva Agrisciences, Johnston, IA 50131, USA
| | - James E Specht
- Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE 68583, USA
| | - William D Beavis
- Department of Agronomy, Iowa State University, Agronomy Hall, Ames, IA 50011, USA
| | - Carl J Bernacchi
- Global Change and Photosynthesis Research Unit, USDA ARS, Urbana, IL 61801, USA
- Carl R. Woese Institute for Genomic Biology, Urbana, IL 61801, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Brian W Diers
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Elizabeth A Ainsworth
- Global Change and Photosynthesis Research Unit, USDA ARS, Urbana, IL 61801, USA
- Carl R. Woese Institute for Genomic Biology, Urbana, IL 61801, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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20
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Fu P, Montes CM, Siebers MH, Gomez-Casanovas N, McGrath JM, Ainsworth EA, Bernacchi CJ. Advances in field-based high-throughput photosynthetic phenotyping. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:3157-3172. [PMID: 35218184 PMCID: PMC9126737 DOI: 10.1093/jxb/erac077] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 02/23/2022] [Indexed: 05/22/2023]
Abstract
Gas exchange techniques revolutionized plant research and advanced understanding, including associated fluxes and efficiencies, of photosynthesis, photorespiration, and respiration of plants from cellular to ecosystem scales. These techniques remain the gold standard for inferring photosynthetic rates and underlying physiology/biochemistry, although their utility for high-throughput phenotyping (HTP) of photosynthesis is limited both by the number of gas exchange systems available and the number of personnel available to operate the equipment. Remote sensing techniques have long been used to assess ecosystem productivity at coarse spatial and temporal resolutions, and advances in sensor technology coupled with advanced statistical techniques are expanding remote sensing tools to finer spatial scales and increasing the number and complexity of phenotypes that can be extracted. In this review, we outline the photosynthetic phenotypes of interest to the plant science community and describe the advances in high-throughput techniques to characterize photosynthesis at spatial scales useful to infer treatment or genotypic variation in field-based experiments or breeding trials. We will accomplish this objective by presenting six lessons learned thus far through the development and application of proximal/remote sensing-based measurements and the accompanying statistical analyses. We will conclude by outlining what we perceive as the current limitations, bottlenecks, and opportunities facing HTP of photosynthesis.
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Affiliation(s)
- Peng Fu
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Christopher M Montes
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- United States Department of Agriculture, Global Change and Photosynthesis Research Unit, Agricultural Research Service, Urbana, IL, USA
| | - Matthew H Siebers
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- United States Department of Agriculture, Global Change and Photosynthesis Research Unit, Agricultural Research Service, Urbana, IL, USA
| | - Nuria Gomez-Casanovas
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Institute for Sustainability, Energy & Environment, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Justin M McGrath
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- United States Department of Agriculture, Global Change and Photosynthesis Research Unit, Agricultural Research Service, Urbana, IL, USA
| | - Elizabeth A Ainsworth
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- United States Department of Agriculture, Global Change and Photosynthesis Research Unit, Agricultural Research Service, Urbana, IL, USA
- Institute for Sustainability, Energy & Environment, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Carl J Bernacchi
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- United States Department of Agriculture, Global Change and Photosynthesis Research Unit, Agricultural Research Service, Urbana, IL, USA
- Institute for Sustainability, Energy & Environment, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Correspondence:
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21
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Sharwood RE, Quick WP, Sargent D, Estavillo GM, Silva-Perez V, Furbank RT. Mining for allelic gold: finding genetic variation in photosynthetic traits in crops and wild relatives. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:3085-3108. [PMID: 35274686 DOI: 10.1093/jxb/erac081] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 02/28/2022] [Indexed: 06/14/2023]
Abstract
Improvement of photosynthetic traits in crops to increase yield potential and crop resilience has recently become a major breeding target. Synthetic biology and genetic technologies offer unparalleled opportunities to create new genetics for photosynthetic traits driven by existing fundamental knowledge. However, large 'gene bank' collections of germplasm comprising historical collections of crop species and their relatives offer a wealth of opportunities to find novel allelic variation in the key steps of photosynthesis, to identify new mechanisms and to accelerate genetic progress in crop breeding programmes. Here we explore the available genetic resources in food and fibre crops, strategies to selectively target allelic variation in genes underpinning key photosynthetic processes, and deployment of this variation via gene editing in modern elite material.
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Affiliation(s)
- Robert E Sharwood
- Hawkesbury Institute for the Environment, Western Sydney University, Richmond, NSW, Australia
- ARC Centre of Excellence for Translational Photosynthesis, Research School of Biology, Australian National University, Canberra, ACT, Australia
| | - W Paul Quick
- ARC Centre of Excellence for Translational Photosynthesis, Research School of Biology, Australian National University, Canberra, ACT, Australia
- International Rice Research Institute, Los Baños, Laguna, Philippines
| | - Demi Sargent
- Hawkesbury Institute for the Environment, Western Sydney University, Richmond, NSW, Australia
| | | | | | - Robert T Furbank
- ARC Centre of Excellence for Translational Photosynthesis, Research School of Biology, Australian National University, Canberra, ACT, Australia
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22
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Sales CRG, Molero G, Evans JR, Taylor SH, Joynson R, Furbank RT, Hall A, Carmo-Silva E. Phenotypic variation in photosynthetic traits in wheat grown under field versus glasshouse conditions. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:3221-3237. [PMID: 35271722 PMCID: PMC9126738 DOI: 10.1093/jxb/erac096] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 03/08/2022] [Indexed: 05/19/2023]
Abstract
Recognition of the untapped potential of photosynthesis to improve crop yields has spurred research to identify targets for breeding. The CO2-fixing enzyme Rubisco is characterized by a number of inefficiencies, and frequently limits carbon assimilation at the top of the canopy, representing a clear target for wheat improvement. Two bread wheat lines with similar genetic backgrounds and contrasting in vivo maximum carboxylation activity of Rubisco per unit leaf nitrogen (Vc,max,25/Narea) determined using high-throughput phenotyping methods were selected for detailed study from a panel of 80 spring wheat lines. Detailed phenotyping of photosynthetic traits in the two lines using glasshouse-grown plants showed no difference in Vc,max,25/Narea determined directly via in vivo and in vitro methods. Detailed phenotyping of glasshouse-grown plants of the 80 wheat lines also showed no correlation between photosynthetic traits measured via high-throughput phenotyping of field-grown plants. Our findings suggest that the complex interplay between traits determining crop productivity and the dynamic environments experienced by field-grown plants needs to be considered in designing strategies for effective wheat crop yield improvement when breeding for particular environments.
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Affiliation(s)
- Cristina R G Sales
- Lancaster Environment Centre, Lancaster University, Library Avenue, Lancaster LA1 4YQ, UK
- Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK
- Correspondence: or
| | - Gemma Molero
- International Maize and Wheat Improvement Centre (CIMMYT), Int. Apdo. Postal 6-641, 06600 Mexico, DF, Mexico
- KWS Momont Recherche, 7 rue de Martinval, 59246 Mons-en-Pévèle, France
| | - John R Evans
- ARC Centre of Excellence for Translational Photosynthesis, Research School of Biology, The Australian National University, Canberra ACT 2601, Australia
| | - Samuel H Taylor
- Lancaster Environment Centre, Lancaster University, Library Avenue, Lancaster LA1 4YQ, UK
| | - Ryan Joynson
- Organisms and Ecosystems, Earlham Institute, Norwich Research Park, Norwich NR4 7UG, UK
- Limagrain Europe, CS 3911, 63720 Chappes, France
| | - Robert T Furbank
- ARC Centre of Excellence for Translational Photosynthesis, Research School of Biology, The Australian National University, Canberra ACT 2601, Australia
| | - Anthony Hall
- Organisms and Ecosystems, Earlham Institute, Norwich Research Park, Norwich NR4 7UG, UK
| | - Elizabete Carmo-Silva
- Lancaster Environment Centre, Lancaster University, Library Avenue, Lancaster LA1 4YQ, UK
- Correspondence: or
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23
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Posch BC, Hammer J, Atkin OK, Bramley H, Ruan YL, Trethowan R, Coast O. Wheat photosystem II heat tolerance responds dynamically to short- and long-term warming. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:erac039. [PMID: 35604885 PMCID: PMC9127437 DOI: 10.1093/jxb/erac039] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 01/31/2022] [Indexed: 05/10/2023]
Abstract
Wheat photosynthetic heat tolerance can be characterized using minimal chlorophyll fluorescence to quantify the critical temperature (Tcrit) above which incipient damage to the photosynthetic machinery occurs. We investigated intraspecies variation and plasticity of wheat Tcrit under elevated temperature in field and controlled-environment experiments, and assessed whether intraspecies variation mirrored interspecific patterns of global heat tolerance. In the field, wheat Tcrit varied diurnally-declining from noon through to sunrise-and increased with phenological development. Under controlled conditions, heat stress (36 °C) drove a rapid (within 2 h) rise in Tcrit that peaked after 3-4 d. The peak in Tcrit indicated an upper limit to PSII heat tolerance. A global dataset [comprising 183 Triticum and wild wheat (Aegilops) species] generated from the current study and a systematic literature review showed that wheat leaf Tcrit varied by up to 20 °C (roughly two-thirds of reported global plant interspecies variation). However, unlike global patterns of interspecies Tcrit variation that have been linked to latitude of genotype origin, intraspecific variation in wheat Tcrit was unrelated to that. Overall, the observed genotypic variation and plasticity of wheat Tcrit suggest that this trait could be useful in high-throughput phenotyping of wheat photosynthetic heat tolerance.
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Affiliation(s)
- Bradley C Posch
- ARC Centre of Excellence in Plant Energy Biology, Division of Plant Sciences, Research School of Biology, The Australian National University, Canberra, ACT 2601, Australia
| | - Julia Hammer
- ARC Centre of Excellence in Plant Energy Biology, Division of Plant Sciences, Research School of Biology, The Australian National University, Canberra, ACT 2601, Australia
- Department of Biology, The University of Western Ontario, 1151 Richmond St, N6A 3K7, London, Canada
| | - Owen K Atkin
- ARC Centre of Excellence in Plant Energy Biology, Division of Plant Sciences, Research School of Biology, The Australian National University, Canberra, ACT 2601, Australia
| | - Helen Bramley
- Plant Breeding Institute, Sydney Institute of Agriculture & School of Life and Environmental Sciences, The University of Sydney, Narrabri, NSW 2390, Australia
| | - Yong-Ling Ruan
- Australia-China Research Centre for Crop Improvement and School of Environmental and Life Sciences, The University of Newcastle, Callaghan, NSW 2308, Australia
| | - Richard Trethowan
- Plant Breeding Institute, Sydney Institute of Agriculture & School of Life and Environmental Sciences, The University of Sydney, Narrabri, NSW 2390, Australia
- School of Life and Environmental Sciences, Plant Breeding Institute, Sydney Institute of Agriculture, The University of Sydney, Cobbitty, NSW 2570, Australia
| | - Onoriode Coast
- ARC Centre of Excellence in Plant Energy Biology, Division of Plant Sciences, Research School of Biology, The Australian National University, Canberra, ACT 2601, Australia
- Natural Resources Institute, University of Greenwich, Central Avenue, Chatham Maritime, Kent ME4 4TB, UK
- School of Environmental and Rural Sciences, University of New England, Armidale, NSW 2351, Australia
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24
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Vasseur F, Cornet D, Beurier G, Messier J, Rouan L, Bresson J, Ecarnot M, Stahl M, Heumos S, Gérard M, Reijnen H, Tillard P, Lacombe B, Emanuel A, Floret J, Estarague A, Przybylska S, Sartori K, Gillespie LM, Baron E, Kazakou E, Vile D, Violle C. A Perspective on Plant Phenomics: Coupling Deep Learning and Near-Infrared Spectroscopy. FRONTIERS IN PLANT SCIENCE 2022; 13:836488. [PMID: 35668791 PMCID: PMC9163986 DOI: 10.3389/fpls.2022.836488] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 03/09/2022] [Indexed: 05/31/2023]
Abstract
The trait-based approach in plant ecology aims at understanding and classifying the diversity of ecological strategies by comparing plant morphology and physiology across organisms. The major drawback of the approach is that the time and financial cost of measuring the traits on many individuals and environments can be prohibitive. We show that combining near-infrared spectroscopy (NIRS) with deep learning resolves this limitation by quickly, non-destructively, and accurately measuring a suite of traits, including plant morphology, chemistry, and metabolism. Such an approach also allows to position plants within the well-known CSR triangle that depicts the diversity of plant ecological strategies. The processing of NIRS through deep learning identifies the effect of growth conditions on trait values, an issue that plagues traditional statistical approaches. Together, the coupling of NIRS and deep learning is a promising high-throughput approach to capture a range of ecological information on plant diversity and functioning and can accelerate the creation of extensive trait databases.
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Affiliation(s)
| | - Denis Cornet
- CIRAD, UMR AGAP Institut, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Grégory Beurier
- CIRAD, UMR AGAP Institut, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Julie Messier
- Department of Biology, University of Waterloo, Waterloo, ON, Canada
| | - Lauriane Rouan
- CIRAD, UMR AGAP Institut, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Justine Bresson
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
| | - Martin Ecarnot
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Mark Stahl
- Center for Plant Molecular Biology (ZMBP), University of Tübingen, Tübingen, Germany
| | - Simon Heumos
- Quantitative Biology Center (QBiC), University of Tübingen, Quantitative Biology Center (QBiC), University of Tübingen, Germany
- Biomedical Data Science, Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Marianne Gérard
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
| | - Hans Reijnen
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
| | - Pascal Tillard
- BPMP, Univ Montpellier, CNRS, INRAE, Montpellier, France
| | - Benoît Lacombe
- BPMP, Univ Montpellier, CNRS, INRAE, Montpellier, France
| | - Amélie Emanuel
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
- BPMP, Univ Montpellier, CNRS, INRAE, Montpellier, France
| | - Justine Floret
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, France
| | | | | | - Kevin Sartori
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
| | | | - Etienne Baron
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
| | - Elena Kazakou
- CEFE, Univ Montpellier, CNRS, EPHE, Institut Agro, IRD, Montpellier, France
| | - Denis Vile
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, France
| | - Cyrille Violle
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
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25
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Prediction of Potassium in Peach Leaves Using Hyperspectral Imaging and Multivariate Analysis. AGRIENGINEERING 2022. [DOI: 10.3390/agriengineering4020027] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
Abstract
Hyperspectral imaging (HSI) is an emerging technology being utilized in agriculture. This system could be used to monitor the overall health of plants or in pest/disease detection. As sensing technology advancement expands, measuring nutrient levels and disease detection also progresses. This study aimed to predict three different levels of potassium (K) concentration in peach leaves using principal component analysis (PCA) and develop models for predicting the K concentration of a peach leaf using a hyperspectral imaging technique. Hyperspectral images were acquired from a randomly selected fresh peach leaf from multiple trees over the spectral region between 500 and 900 nm. Leaves were collected from trees with varying potassium levels of high (2.7~3.2%), medium (2.0~2.6%), and low (1.3~1.9%). Four pretreatment methods (multiplicative scatter effect (MSC), Savitzky–Golay first derivative, Savitzky–Golay second derivative, and standard normal variate (SNV)) were applied to the raw data and partial least square (PLS) was used to develop a model for each of the pretreatments. The R2 values for each pretreatment method were 0.8099, 0.6723, 0.5586, and 0.8446, respectively. The SNV prediction model has the highest accuracy and was used to predict the K nutrient using the validation data. The result showed a slightly lower R2 = 0.8101 compared with the training. This study showed that HSI could measure K concentration in peach tree cultivars.
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26
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Robles-Zazueta CA, Pinto F, Molero G, Foulkes MJ, Reynolds MP, Murchie EH. Prediction of Photosynthetic, Biophysical, and Biochemical Traits in Wheat Canopies to Reduce the Phenotyping Bottleneck. FRONTIERS IN PLANT SCIENCE 2022; 13:828451. [PMID: 35481146 PMCID: PMC9036448 DOI: 10.3389/fpls.2022.828451] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 01/25/2022] [Indexed: 06/14/2023]
Abstract
To achieve food security, it is necessary to increase crop radiation use efficiency (RUE) and yield through the enhancement of canopy photosynthesis to increase the availability of assimilates for the grain, but its study in the field is constrained by low throughput and the lack of integrative measurements at canopy level. In this study, partial least squares regression (PLSR) was used with high-throughput phenotyping (HTP) data in spring wheat to build predictive models of photosynthetic, biophysical, and biochemical traits for the top, middle, and bottom layers of wheat canopies. The combined layer model predictions performed better than individual layer predictions with a significance as follows for photosynthesis R 2 = 0.48, RMSE = 5.24 μmol m-2 s-1 and stomatal conductance: R 2 = 0.36, RMSE = 0.14 mol m-2 s-1. The predictions of these traits from PLSR models upscaled to canopy level compared to field observations were statistically significant at initiation of booting (R 2 = 0.3, p < 0.05; R 2 = 0.29, p < 0.05) and at 7 days after anthesis (R 2 = 0.15, p < 0.05; R 2 = 0.65, p < 0.001). Using HTP allowed us to increase phenotyping capacity 30-fold compared to conventional phenotyping methods. This approach can be adapted to screen breeding progeny and genetic resources for RUE and to improve our understanding of wheat physiology by adding different layers of the canopy to physiological modeling.
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Affiliation(s)
- Carlos A. Robles-Zazueta
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Leicestershire, United Kingdom
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Francisco Pinto
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Gemma Molero
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - M. John Foulkes
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Leicestershire, United Kingdom
| | - Matthew P. Reynolds
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Erik H. Murchie
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Leicestershire, United Kingdom
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27
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Buchaillot ML, Soba D, Shu T, Liu J, Aranjuelo I, Araus JL, Runion GB, Prior SA, Kefauver SC, Sanz-Saez A. Estimating peanut and soybean photosynthetic traits using leaf spectral reflectance and advance regression models. PLANTA 2022; 255:93. [PMID: 35325309 PMCID: PMC8948130 DOI: 10.1007/s00425-022-03867-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 03/03/2022] [Indexed: 06/14/2023]
Abstract
MAIN CONCLUSION By combining hyperspectral signatures of peanut and soybean, we predicted Vcmax and Jmax with 70 and 50% accuracy. The PLS was the model that better predicted these photosynthetic parameters. One proposed key strategy for increasing potential crop stability and yield centers on exploitation of genotypic variability in photosynthetic capacity through precise high-throughput phenotyping techniques. Photosynthetic parameters, such as the maximum rate of Rubisco catalyzed carboxylation (Vc,max) and maximum electron transport rate supporting RuBP regeneration (Jmax), have been identified as key targets for improvement. The primary techniques for measuring these physiological parameters are very time-consuming. However, these parameters could be estimated using rapid and non-destructive leaf spectroscopy techniques. This study compared four different advanced regression models (PLS, BR, ARDR, and LASSO) to estimate Vc,max and Jmax based on leaf reflectance spectra measured with an ASD FieldSpec4. Two leguminous species were tested under different controlled environmental conditions: (1) peanut under different water regimes at normal atmospheric conditions and (2) soybean under high [CO2] and high night temperature. Model sensitivities were assessed for each crop and treatment separately and in combination to identify strengths and weaknesses of each modeling approach. Regardless of regression model, robust predictions were achieved for Vc,max (R2 = 0.70) and Jmax (R2 = 0.50). Field spectroscopy shows promising results for estimating spatial and temporal variations in photosynthetic capacity based on leaf and canopy spectral properties.
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Affiliation(s)
- Ma Luisa Buchaillot
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028, Barcelona, Spain
- AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, 25198, Lleida, Spain
| | - David Soba
- Instituto de Agrobiotecnología (IdAB), Consejo Superior de Investigaciones Científicas (CSIC)-Gobierno de Navarra, Av. Pamplona 123, 31192, Mutilva, Spain
| | - Tianchu Shu
- Department of Crop, Soil, and Environmental Sciences, Auburn University, Alabama, USA
| | - Juan Liu
- Industrial Crops Research Institute, Henan Academy of Agricultural Sciences, Henan, China
| | - Iker Aranjuelo
- Instituto de Agrobiotecnología (IdAB), Consejo Superior de Investigaciones Científicas (CSIC)-Gobierno de Navarra, Av. Pamplona 123, 31192, Mutilva, Spain
| | - José Luis Araus
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028, Barcelona, Spain
- AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, 25198, Lleida, Spain
| | - G Brett Runion
- U.S. Department of Agriculture-Agricultural Research Service, National Soil Dynamics Laboratory, Auburn, AL, 36832, USA
| | - Stephen A Prior
- U.S. Department of Agriculture-Agricultural Research Service, National Soil Dynamics Laboratory, Auburn, AL, 36832, USA
| | - Shawn C Kefauver
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028, Barcelona, Spain.
- AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, 25198, Lleida, Spain.
| | - Alvaro Sanz-Saez
- Department of Crop, Soil, and Environmental Sciences, Auburn University, Alabama, USA.
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28
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Tanner F, Tonn S, de Wit J, Van den Ackerveken G, Berger B, Plett D. Sensor-based phenotyping of above-ground plant-pathogen interactions. PLANT METHODS 2022; 18:35. [PMID: 35313920 PMCID: PMC8935837 DOI: 10.1186/s13007-022-00853-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 02/08/2022] [Indexed: 05/20/2023]
Abstract
Plant pathogens cause yield losses in crops worldwide. Breeding for improved disease resistance and management by precision agriculture are two approaches to limit such yield losses. Both rely on detecting and quantifying signs and symptoms of plant disease. To achieve this, the field of plant phenotyping makes use of non-invasive sensor technology. Compared to invasive methods, this can offer improved throughput and allow for repeated measurements on living plants. Abiotic stress responses and yield components have been successfully measured with phenotyping technologies, whereas phenotyping methods for biotic stresses are less developed, despite the relevance of plant disease in crop production. The interactions between plants and pathogens can lead to a variety of signs (when the pathogen itself can be detected) and diverse symptoms (detectable responses of the plant). Here, we review the strengths and weaknesses of a broad range of sensor technologies that are being used for sensing of signs and symptoms on plant shoots, including monochrome, RGB, hyperspectral, fluorescence, chlorophyll fluorescence and thermal sensors, as well as Raman spectroscopy, X-ray computed tomography, and optical coherence tomography. We argue that choosing and combining appropriate sensors for each plant-pathosystem and measuring with sufficient spatial resolution can enable specific and accurate measurements of above-ground signs and symptoms of plant disease.
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Affiliation(s)
- Florian Tanner
- Australian Plant Phenomics Facility, School of Agriculture, Food and Wine, University of Adelaide, Urrbrae, SA Australia
| | - Sebastian Tonn
- Department of Biology, Plant-Microbe Interactions, Utrecht University, 3584CH Utrecht, The Netherlands
| | - Jos de Wit
- Department of Imaging Physics, Delft University of Technology, Lorentzweg 1, 2628 CJ Delft, The Netherlands
| | - Guido Van den Ackerveken
- Department of Biology, Plant-Microbe Interactions, Utrecht University, 3584CH Utrecht, The Netherlands
| | - Bettina Berger
- Australian Plant Phenomics Facility, School of Agriculture, Food and Wine, University of Adelaide, Urrbrae, SA Australia
| | - Darren Plett
- Australian Plant Phenomics Facility, School of Agriculture, Food and Wine, University of Adelaide, Urrbrae, SA Australia
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Wu W, Tang T, Gao T, Han C, Li J, Zhang Y, Wang X, Wang J, Feng Y. Spectral Diagnostic Model for Agricultural Robot System Based on Binary Wavelet Algorithm. SENSORS (BASEL, SWITZERLAND) 2022; 22:1822. [PMID: 35270973 PMCID: PMC8914903 DOI: 10.3390/s22051822] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 02/17/2022] [Accepted: 02/22/2022] [Indexed: 06/14/2023]
Abstract
The application of agricultural robots can liberate labor. The improvement of robot sensing systems is the premise of making it work. At present, more research is being conducted on weeding and harvesting systems of field robot, but less research is being conducted on crop disease and insect pest perception, nutritional element diagnosis and precision fertilizer spraying systems. In this study, the effects of the nitrogen application rate on the absorption and accumulation of nitrogen, phosphorus and potassium in sweet maize were determined. Firstly, linear, parabolic, exponential and logarithmic diagnostic models of nitrogen, phosphorus and potassium contents were constructed by spectral characteristic variables. Secondly, the partial least squares regression and neural network nonlinear diagnosis model of nitrogen, phosphorus and potassium contents were constructed by the high-frequency wavelet sensitivity coefficient of binary wavelet decomposition. The results show that the neural network nonlinear diagnosis model of nitrogen, phosphorus and potassium content based on the high-frequency wavelet sensitivity coefficient of binary wavelet decomposition is better. The R2, MRE and NRMSE of nn of nitrogen, phosphorus and potassium were 0.974, 1.65% and 0.0198; 0.969, 9.02% and 0.1041; and 0.821, 2.16% and 0.0301, respectively. The model can provide growth monitoring for sweet corn and a perception model for the nutrient element perception system of an agricultural robot, while making preliminary preparations for the realization of intelligent and accurate field fertilization.
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Affiliation(s)
- Weibin Wu
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China; (W.W.); (T.T.); (C.H.); (J.L.); (Y.Z.)
- College of Engineering, South China Agricultural University, Guangzhou 510642, China
| | - Ting Tang
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China; (W.W.); (T.T.); (C.H.); (J.L.); (Y.Z.)
- College of Engineering, South China Agricultural University, Guangzhou 510642, China
| | - Ting Gao
- College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China; (T.G.); (X.W.); (J.W.)
| | - Chongyang Han
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China; (W.W.); (T.T.); (C.H.); (J.L.); (Y.Z.)
- College of Engineering, South China Agricultural University, Guangzhou 510642, China
| | - Jie Li
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China; (W.W.); (T.T.); (C.H.); (J.L.); (Y.Z.)
- College of Engineering, South China Agricultural University, Guangzhou 510642, China
| | - Ying Zhang
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China; (W.W.); (T.T.); (C.H.); (J.L.); (Y.Z.)
- College of Engineering, South China Agricultural University, Guangzhou 510642, China
| | - Xiaoyi Wang
- College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China; (T.G.); (X.W.); (J.W.)
- Key Laboratory of Agro-Environment in the Tropics, Ministry of Agriculture, South China Agricultural University, Guangzhou 510642, China
- Guangdong Engineering Research Center for Modern Eco-Agriculture and Circular Agriculture, Guangzhou 510642, China
| | - Jianwu Wang
- College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China; (T.G.); (X.W.); (J.W.)
- Key Laboratory of Agro-Environment in the Tropics, Ministry of Agriculture, South China Agricultural University, Guangzhou 510642, China
- Guangdong Engineering Research Center for Modern Eco-Agriculture and Circular Agriculture, Guangzhou 510642, China
| | - Yuanjiao Feng
- College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China; (T.G.); (X.W.); (J.W.)
- Key Laboratory of Agro-Environment in the Tropics, Ministry of Agriculture, South China Agricultural University, Guangzhou 510642, China
- Guangdong Engineering Research Center for Modern Eco-Agriculture and Circular Agriculture, Guangzhou 510642, China
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Combining Hyperspectral Reflectance and Multivariate Regression Models to Estimate Plant Biomass of Advanced Spring Wheat Lines in Diverse Phenological Stages under Salinity Conditions. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12041983] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
An area of growing interest in wheat-breeding programs for abiotic stresses is the accurate and expeditious phenotyping of large genotype collections using nondestructive hyperspectral sensing tools. The main goal of this study was to use data from canopy spectral signatures (CSS) in the full-spectrum range (400–2500 nm) to estimate and predict the plant biomass dry weight at booting (BDW-BT) and anthesis (BDW-AN) growth stages, and biological yield (BY) of 64 spring wheat germplasms exposed to 150 mM NaCl using 13 spectral reflectance indices (SRIs, consisting of seven vegetation-related SRIs and six water-related SRIs) and partial least squares regression (PLSR). SRI and PLSR performance in estimating plant traits was evaluated during two years at BT, AN, and early milk grain (EMG) growth stages. Results showed significant genotypic differences between the three traits and SRIs, with highly significant two-way and three-way interactions between genotypes, years, and growth stages for all SRIs. Genotypic differences in CSS and the relationships between the three traits and a single wavelength over the full-spectrum range depended on the growth stage. Water-related SRIs were more strongly correlated with the three traits compared with vegetation-related SRIs at the BT stage; the opposite was found at the EMG stage. Both types of SRIs exhibited comparable associations with the three traits at the AN stage. Principal component analysis indicated that it is possible to assess plant biomass variations at an early stage (BT) through published and modified SRIs. SRIs coupled with PLSR models at the BT stage exhibited good prediction capacity of BDW-BT (57%), BDW-AN (82%), and BY (55%). Overall, results demonstrated that the integration of SRIs and multivariate models may present a feasible tool for plant breeders to increase the efficiency of the evaluation process and to improve the genetics for salt tolerance in wheat-breeding programs.
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El-Hendawy S, Dewir YH, Elsayed S, Schmidhalter U, Al-Gaadi K, Tola E, Refay Y, Tahir MU, Hassan WM. Combining Hyperspectral Reflectance Indices and Multivariate Analysis to Estimate Different Units of Chlorophyll Content of Spring Wheat under Salinity Conditions. PLANTS (BASEL, SWITZERLAND) 2022; 11:plants11030456. [PMID: 35161437 PMCID: PMC8839343 DOI: 10.3390/plants11030456] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 02/03/2022] [Accepted: 02/03/2022] [Indexed: 05/30/2023]
Abstract
Although plant chlorophyll (Chl) is one of the important elements in monitoring plant stress and reflects the photosynthetic capacity of plants, their measurement in the lab is generally time- and cost-inefficient and based on a small part of the leaf. This study examines the ability of canopy spectral reflectance data for the accurate estimation of the Chl content of two wheat genotypes grown under three salinity levels. The Chl content was quantified as content per area (Chl area, μg cm-2), concentration per plant (Chl plant, mg plant-1), and SPAD value (Chl SPAD). The performance of spectral reflectance indices (SRIs) with different algorithm forms, partial least square regression (PLSR), and stepwise multiple linear regression (SMLR) in estimating the three units of Chl content was compared. Results show that most indices within each SRI form performed better with Chl area and Chl plant and performed poorly with Chl SPAD. The PLSR models, based on the four forms of SRIs individually or combined, still performed poorly in estimating Chl SPAD, while they exhibited a strong relationship with Chl plant followed by Chl area in both the calibration (Cal.) and validation (Val.) datasets. The SMLR models extracted three to four indices from each SRI form as the most effective indices and explained 73-79%, 80-84%, and 39-43% of the total variability in Chl area, Chl plant, and Chl SPAD, respectively. The performance of the various predictive models of SMLR for predicting Chl content depended on salinity level, genotype, season, and the units of Chl content. In summary, this study indicates that the Chl content measured in the lab and expressed on content (μg cm-2) or concentration (mg plant-1) can be accurately estimated at canopy level using spectral reflectance data.
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Affiliation(s)
- Salah El-Hendawy
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, KSA, P.O. Box 2460, Riyadh 11451, Saudi Arabia; (Y.H.D.); (Y.R.); (M.U.T.)
| | - Yaser Hassan Dewir
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, KSA, P.O. Box 2460, Riyadh 11451, Saudi Arabia; (Y.H.D.); (Y.R.); (M.U.T.)
| | - Salah Elsayed
- Agricultural Engineering, Evaluation of Natural Resources Department, Environmental Studies and Research Institute, University of Sadat City, Sadat City 32897, Egypt;
| | - Urs Schmidhalter
- Chair of Plant Nutrition, Department of Plant Sciences, Technical University of Munich, Emil-Ramann-Str. 2, D-85350 Munich, Germany;
| | - Khalid Al-Gaadi
- Department of Agricultural Engineering, Precision Agriculture Research Chair (PARC), College of Food and Agriculture Sciences, King Saud University, KSA, P.O. Box 2460, Riyadh 11451, Saudi Arabia; (K.A.-G.); (E.T.)
| | - ElKamil Tola
- Department of Agricultural Engineering, Precision Agriculture Research Chair (PARC), College of Food and Agriculture Sciences, King Saud University, KSA, P.O. Box 2460, Riyadh 11451, Saudi Arabia; (K.A.-G.); (E.T.)
| | - Yahya Refay
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, KSA, P.O. Box 2460, Riyadh 11451, Saudi Arabia; (Y.H.D.); (Y.R.); (M.U.T.)
| | - Muhammad Usman Tahir
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, KSA, P.O. Box 2460, Riyadh 11451, Saudi Arabia; (Y.H.D.); (Y.R.); (M.U.T.)
| | - Wael M. Hassan
- Department of Agricultural Botany, Faculty of Agriculture, Suez Canal University, Ismailia 41522, Egypt;
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32
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Characteristics of chlorophyll fluorescence in ten garden shrub species under flooding stress. Biologia (Bratisl) 2022. [DOI: 10.1007/s11756-021-00947-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Jin J, Wang Q, Song G. Selecting informative bands for partial least squares regressions improves their goodness-of-fits to estimate leaf photosynthetic parameters from hyperspectral data. PHOTOSYNTHESIS RESEARCH 2022; 151:71-82. [PMID: 34491493 DOI: 10.1007/s11120-021-00873-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 08/24/2021] [Indexed: 06/13/2023]
Abstract
The plant photosynthetic capacity determines the photosynthetic rates of the terrestrial biosphere. Timely approaches to obtain the spatiotemporal variations of the photosynthetic parameters are urgently needed to grasp the gas exchange rhythms of the terrestrial biosphere. While partial least squares regression (PLSR) is a promising way to predict the photosynthetic parameters maximum carboxylation rate (Vcmax) and maximum electron transport rate (Jmax) rapidly and non-destructively from hyperspectral data, the approach, however, faces a high risk of overfitting and remains a high hurdle for applications. In this study, we propose to incorporate proper band selection techniques for PLSR analysis to refine the goodness-of-fit (GoF) in estimating Vcmax and Jmax. Different band selection procedures coupled with different hyperspectral forms (reflectance, apparent absorption, as well as derivatives) were examined. Our results demonstrate that the GoFs of PLSR models could be greatly improved by combining proper band selection methods (especially the iterative stepwise elimination approach) rather than using full bands as commonly done with PLSR. The results also show that the 1st order derivative spectra had a balance between accuracy (R2 = 0.80 for Vcmax, and 0.94 for Jmax) and denoising (when a Gaussian noise was added to each leaf reflectance spectrum at each wavelength with a standard deviation of 1%) on retrieving photosynthetic parameters from hyperspectral data. Our results clearly illustrate the advantage of using the band selection approach for PLSR dimensionality reduction and model optimization, highlighting the superiority of using derivative spectra for Vcmax and Jmax estimations, which should provide valuable insights for retrieving photosynthetic parameters from hyperspectral remotely sensed data.
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Affiliation(s)
- Jia Jin
- Faculty of Agriculture, Shizuoka University, Shizuoka, 422-8529, Japan
- Institute of Geography and Oceanography, Nanning Normal University, Nanning, 530001, China
| | - Quan Wang
- Faculty of Agriculture, Shizuoka University, Shizuoka, 422-8529, Japan.
- Research Institute of Green Science and Technology, Shizuoka University, Shizuoka, 422-8529, Japan.
| | - Guangman Song
- Graduate School of Science and Technology, Shizuoka University, Shizuoka, 422-8529, Japan
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34
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Zhi X, Massey-Reed SR, Wu A, Potgieter A, Borrell A, Hunt C, Jordan D, Zhao Y, Chapman S, Hammer G, George-Jaeggli B. Estimating Photosynthetic Attributes from High-Throughput Canopy Hyperspectral Sensing in Sorghum. PLANT PHENOMICS (WASHINGTON, D.C.) 2022; 2022:9768502. [PMID: 35498954 PMCID: PMC9013486 DOI: 10.34133/2022/9768502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 02/25/2022] [Indexed: 05/04/2023]
Abstract
Sorghum, a genetically diverse C4 cereal, is an ideal model to study natural variation in photosynthetic capacity. Specific leaf nitrogen (SLN) and leaf mass per leaf area (LMA), as well as, maximal rates of Rubisco carboxylation (V cmax), phosphoenolpyruvate (PEP) carboxylation (V pmax), and electron transport (J max), quantified using a C4 photosynthesis model, were evaluated in two field-grown training sets (n = 169 plots including 124 genotypes) in 2019 and 2020. Partial least square regression (PLSR) was used to predict V cmax (R 2 = 0.83), V pmax (R 2 = 0.93), J max (R 2 = 0.76), SLN (R 2 = 0.82), and LMA (R 2 = 0.68) from tractor-based hyperspectral sensing. Further assessments of the capability of the PLSR models for V cmax, V pmax, J max, SLN, and LMA were conducted by extrapolating these models to two trials of genome-wide association studies adjacent to the training sets in 2019 (n = 875 plots including 650 genotypes) and 2020 (n = 912 plots with 634 genotypes). The predicted traits showed medium to high heritability and genome-wide association studies using the predicted values identified four QTL for V cmax and two QTL for J max. Candidate genes within 200 kb of the V cmax QTL were involved in nitrogen storage, which is closely associated with Rubisco, while not directly associated with Rubisco activity per se. J max QTL was enriched for candidate genes involved in electron transport. These outcomes suggest the methods here are of great promise to effectively screen large germplasm collections for enhanced photosynthetic capacity.
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Affiliation(s)
- Xiaoyu Zhi
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Hermitage Research Facility, Warwick, QLD, Australia
| | - Sean Reynolds Massey-Reed
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Hermitage Research Facility, Warwick, QLD, Australia
| | - Alex Wu
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), St Lucia, QLD, Australia
| | - Andries Potgieter
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Gatton, QLD, Australia
| | - Andrew Borrell
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Hermitage Research Facility, Warwick, QLD, Australia
| | - Colleen Hunt
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Hermitage Research Facility, Warwick, QLD, Australia
- Agri-Science Queensland, Department of Agriculture and Fisheries (DAF), Hermitage Research Facility, Warwick, QLD, Australia
| | - David Jordan
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Hermitage Research Facility, Warwick, QLD, Australia
| | - Yan Zhao
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), St Lucia, QLD, Australia
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Gatton, QLD, Australia
| | - Scott Chapman
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), St Lucia, QLD, Australia
- School of Agriculture and Food Sciences, The University of Queensland, Gatton, QLD, Australia
| | - Graeme Hammer
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), St Lucia, QLD, Australia
| | - Barbara George-Jaeggli
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Hermitage Research Facility, Warwick, QLD, Australia
- Agri-Science Queensland, Department of Agriculture and Fisheries (DAF), Hermitage Research Facility, Warwick, QLD, Australia
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Kumagai E, Burroughs CH, Pederson TL, Montes CM, Peng B, Kimm H, Guan K, Ainsworth EA, Bernacchi CJ. Predicting biochemical acclimation of leaf photosynthesis in soybean under in-field canopy warming using hyperspectral reflectance. PLANT, CELL & ENVIRONMENT 2022; 45:80-94. [PMID: 34664281 DOI: 10.1111/pce.14204] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 10/04/2021] [Accepted: 10/05/2021] [Indexed: 06/13/2023]
Abstract
Traditional gas exchange measurements are cumbersome, which makes it difficult to capture variation in biochemical parameters, namely the maximum rate of carboxylation measured at a reference temperature (Vcmax25 ) and the maximum electron transport at a reference temperature (Jmax25 ), in response to growth temperature over time from days to weeks. Hyperspectral reflectance provides reliable measures of Vcmax25 and Jmax25 ; however, the capability of this method to capture biochemical acclimations of the two parameters to high growth temperature over time has not been demonstrated. In this study, Vcmax25 and Jmax25 were measured over multiple growth stages during two growing seasons for field-grown soybeans using both gas exchange techniques and leaf spectral reflectance under ambient and four elevated canopy temperature treatments (ambient+1.5, +3, +4.5, and +6°C). Spectral vegetation indices and machine learning methods were used to build predictive models for Vcmax25 and Jmax25 , based on the leaf reflectance. Results showed that these models yielded an R2 of 0.57-0.65 and 0.48-0.58 for Vcmax25 and Jmax25 , respectively. Hyperspectral reflectance captured biochemical acclimation of leaf photosynthesis to high temperature in the field, improving spatial and temporal resolution in the ability to assess the impact of future warming on crop productivity.
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Affiliation(s)
- Etsushi Kumagai
- Tohoku Agricultural Research Center, National Agriculture and Food Research Organization, Morioka, Japan
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Charles H Burroughs
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Taylor L Pederson
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Christopher M Montes
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Bin Peng
- College of Agricultural, Consumer and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Hyungsuk Kimm
- College of Agricultural, Consumer and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Kaiyu Guan
- College of Agricultural, Consumer and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Elizabeth A Ainsworth
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Global Change and Photosynthesis Research Unit, USDA-ARS, Urbana, Illinois, USA
| | - Carl J Bernacchi
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Global Change and Photosynthesis Research Unit, USDA-ARS, Urbana, Illinois, USA
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Pane C, Galieni A, Riefolo C, Nicastro N, Castrignanò A. Hyperspectral Reflectance Response of Wild Rocket ( Diplotaxis tenuifolia) Baby-Leaf to Bio-Based Disease Resistance Inducers Using a Linear Mixed Effect Model. PLANTS (BASEL, SWITZERLAND) 2021; 10:plants10122575. [PMID: 34961046 PMCID: PMC8707134 DOI: 10.3390/plants10122575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 11/23/2021] [Accepted: 11/24/2021] [Indexed: 06/14/2023]
Abstract
Baby leaf wild rocket cropping systems feeding the high convenience salad chain are prone to a set of disease agents that require management measures compatible with the sustainability-own features of the ready-to-eat food segment. In this light, bio-based disease resistance inducers able to elicit the plant's defense mechanism(s) against a wide-spectrum of pathogens are proposed as safe and effective remedies as alternatives to synthetic fungicides, to be, however, implemented under practical field applications. Hyperspectral-based proximal sensing was applied here to detect plant reflectance response to treatment of wild rocket beds with Trichoderma atroviride strain TA35, laminarin-based Vacciplant®, and Saccharomyces cerevisiae strain LAS117 cell wall extract-based Romeo®, compared to a local standard approach including synthetic fungicides (i.e., cyprodinil, fludioxonil, mandipropamid, and metalaxyl-m) and a not-treated control. Variability of the spectral information acquired in VIS-NIR-SWIR regions per treatment was explained by three principal components associated with foliar absorption of water, structural characteristics of the vegetation, and the ecophysiological plant status. Therefore, the following model-based statistical approach returned the interpretation of the inducers' performances at field scale consistent with their putative biological effects. The study stated that compost and laminarin-based treatments were the highest crop impacting ones, resulting in enhanced water intake and in stress-related pigment adjustment, respectively. Whereas plants under the conventional chemical management proved to be in better vigor and health status than the untreated control.
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Affiliation(s)
- Catello Pane
- Council for Agricultural Research and Economics (CREA), Research Centre for Vegetable and Ornamental Crops, Via Cavalleggeri 25, 84098 Pontecagnano Faiano, Italy;
| | - Angelica Galieni
- Council for Agricultural Research and Economics (CREA), Research Centre for Vegetable and Ornamental Crops, Via Salaria 1, 63030 Monsampolo del Tronto, Italy;
| | - Carmela Riefolo
- Council for Agricultural Research and Economics (CREA), Research Centre for Agriculture and Environment, Via Celso Ulpiani 5, 70125 Bari, Italy;
| | - Nicola Nicastro
- Council for Agricultural Research and Economics (CREA), Research Centre for Vegetable and Ornamental Crops, Via Cavalleggeri 25, 84098 Pontecagnano Faiano, Italy;
| | - Annamaria Castrignanò
- Department of Engineering and Geology (InGeo), “Gabriele D’Annunzio” University of Chieti-Pescara, Via dei Vestini 31, 66013 Chieti, Italy;
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El-Hendawy S, Al-Suhaibani N, Mubushar M, Tahir MU, Refay Y, Tola E. Potential Use of Hyperspectral Reflectance as a High-Throughput Nondestructive Phenotyping Tool for Assessing Salt Tolerance in Advanced Spring Wheat Lines under Field Conditions. PLANTS (BASEL, SWITZERLAND) 2021; 10:plants10112512. [PMID: 34834875 PMCID: PMC8624136 DOI: 10.3390/plants10112512] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 11/07/2021] [Accepted: 11/16/2021] [Indexed: 06/01/2023]
Abstract
The incorporation of stress tolerance indices (STIs) with the early estimation of grain yield (GY) in an expeditious and nondestructive manner can enable breeders for ensuring the success of genotype development for a wide range of environmental conditions. In this study, the relative performance of GY for sixty-four spring wheat germplasm under the control and 15.0 dS m-1 NaCl were compared through different STIs, and the ability of a hyperspectral reflectance tool for the early estimation of GY and STIs was assessed using twenty spectral reflectance indices (SRIs; 10 vegetation SRIs and 10 water SRIs). The results showed that salinity treatments, genotypes, and their interactions had significant effects on the GY and nearly all SRIs. Significant genotypic variations were also observed for all STIs. Based on the GY under the control (GYc) and salinity (GYs) conditions and all STIs, the tested genotypes were classified into three salinity tolerance groups (salt-tolerant, salt-sensitive, and moderately salt-tolerant groups). Most vegetation and water SRIs showed strong relationships with the GYc, stress tolerance index (STI), and geometric mean productivity (GMP); moderate relationships with GYs and sometimes with the tolerance index (TOL); and weak relationships with the yield stability index (YSI) and stress susceptibility index (SSI). Obvious differences in the spectral reflectance curves were found among the three salinity tolerance groups under the control and salinity conditions. Stepwise multiple linear regressions identified three SRIs from each vegetation and water SRI as the most influential indices that contributed the most variation in the GY. These SRIs were much more effective in estimating the GYc (R2 = 0.64 - 0.79) than GYs (R2 = 0.38 - 0.47). They also provided a much accurate estimation of the GYc and GYs for the moderately salt-tolerant genotype group; YSI, SSI, and TOL for the salt-sensitive genotypes group; and STI and GMP for all the three salinity tolerance groups. Overall, the results of this study highlight the potential of using a hyperspectral reflectance tool in breeding programs for phenotyping a sufficient number of genotypes under a wide range of environmental conditions in a cost-effective, noninvasive, and expeditious manner. This will aid in accelerating the development of genotypes for salinity conditions in breeding programs.
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Affiliation(s)
- Salah El-Hendawy
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia; (N.A.-S.); (M.M.); (M.U.T.); (Y.R.)
- Department of Agronomy, Faculty of Agriculture, Suez Canal University, Ismailia 41522, Egypt
| | - Nasser Al-Suhaibani
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia; (N.A.-S.); (M.M.); (M.U.T.); (Y.R.)
| | - Muhammad Mubushar
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia; (N.A.-S.); (M.M.); (M.U.T.); (Y.R.)
| | - Muhammad Usman Tahir
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia; (N.A.-S.); (M.M.); (M.U.T.); (Y.R.)
| | - Yahya Refay
- Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia; (N.A.-S.); (M.M.); (M.U.T.); (Y.R.)
| | - ElKamil Tola
- Precision Agriculture Research Chair (PARC), College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia;
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Saathoff AJ, Welles J. Gas exchange measurements in the unsteady state. PLANT, CELL & ENVIRONMENT 2021; 44:3509-3523. [PMID: 34480484 PMCID: PMC9292621 DOI: 10.1111/pce.14178] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 08/19/2021] [Indexed: 05/10/2023]
Abstract
Leaf level gas exchange is a widely used technique that provides real-time measurement of leaf physiological properties, including CO2 assimilation (A), stomatal conductance to water vapour (gsw ) and intercellular CO2 (Ci ). Modern open-path gas exchange systems offer greater portability than the laboratory-built systems of the past and take advantage of high-precision infrared gas analyzers and optimized system design. However, the basic measurement paradigm has long required steady-state conditions for accurate measurement. For CO2 response curves, this requirement has meant that each point on the curve needs 1-3 min and a full response curve generally requires 20-35 min to obtain a sufficient number of points to estimate parameters such as the maximum velocity of carboxylation (Vc,max ) and the maximum rate of electron transport (Jmax ). For survey measurements, the steady-state requirement has meant that accurate measurement of assimilation has required about 1-2 min. However, steady-state conditions are not a strict prerequisite for accurate gas exchange measurements. Here, we present a new method, termed dynamic assimilation, that is based on first principles and allows for more rapid gas exchange measurements, helping to make the technique more useful for high throughput applications.
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Affiliation(s)
- Aaron J. Saathoff
- LI‐COR BiosciencesLincolnNebraskaUSA
- School of Natural ResourcesUniversity of NebraskaLincolnNebraskaUSA
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Melandri G, Thorp KR, Broeckling C, Thompson AL, Hinze L, Pauli D. Assessing Drought and Heat Stress-Induced Changes in the Cotton Leaf Metabolome and Their Relationship With Hyperspectral Reflectance. FRONTIERS IN PLANT SCIENCE 2021; 12:751868. [PMID: 34745185 PMCID: PMC8569624 DOI: 10.3389/fpls.2021.751868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 09/30/2021] [Indexed: 06/13/2023]
Abstract
The study of phenotypes that reveal mechanisms of adaptation to drought and heat stress is crucial for the development of climate resilient crops in the face of climate uncertainty. The leaf metabolome effectively summarizes stress-driven perturbations of the plant physiological status and represents an intermediate phenotype that bridges the plant genome and phenome. The objective of this study was to analyze the effect of water deficit and heat stress on the leaf metabolome of 22 genetically diverse accessions of upland cotton grown in the Arizona low desert over two consecutive years. Results revealed that membrane lipid remodeling was the main leaf mechanism of adaptation to drought. The magnitude of metabolic adaptations to drought, which had an impact on fiber traits, was found to be quantitatively and qualitatively associated with different stress severity levels during the two years of the field trial. Leaf-level hyperspectral reflectance data were also used to predict the leaf metabolite profiles of the cotton accessions. Multivariate statistical models using hyperspectral data accurately estimated (R 2 > 0.7 in ∼34% of the metabolites) and predicted (Q 2 > 0.5 in 15-25% of the metabolites) many leaf metabolites. Predicted values of metabolites could efficiently discriminate stressed and non-stressed samples and reveal which regions of the reflectance spectrum were the most informative for predictions. Combined together, these findings suggest that hyperspectral sensors can be used for the rapid, non-destructive estimation of leaf metabolites, which can summarize the plant physiological status.
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Affiliation(s)
- Giovanni Melandri
- School of Plant Sciences, University of Arizona, Tucson, AZ, United States
| | - Kelly R. Thorp
- United States Department of Agriculture-Agricultural Research Service, Arid Land Agricultural Research Center, Maricopa, AZ, United States
| | - Corey Broeckling
- Analytical Resources Core: Bioanalysis and Omics Center, Colorado State University, Fort Collins, CO, United States
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO, United States
| | - Alison L. Thompson
- United States Department of Agriculture-Agricultural Research Service, Arid Land Agricultural Research Center, Maricopa, AZ, United States
| | - Lori Hinze
- United States Department of Agriculture-Agricultural Research Service, Southern Plains Agricultural Research Center, College Station, TX, United States
| | - Duke Pauli
- School of Plant Sciences, University of Arizona, Tucson, AZ, United States
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Furbank RT, Silva-Perez V, Evans JR, Condon AG, Estavillo GM, He W, Newman S, Poiré R, Hall A, He Z. Wheat physiology predictor: predicting physiological traits in wheat from hyperspectral reflectance measurements using deep learning. PLANT METHODS 2021; 17:108. [PMID: 34666801 PMCID: PMC8527791 DOI: 10.1186/s13007-021-00806-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 10/03/2021] [Indexed: 05/06/2023]
Abstract
BACKGROUND The need for rapid in-field measurement of key traits contributing to yield over many thousands of genotypes is a major roadblock in crop breeding. Recently, leaf hyperspectral reflectance data has been used to train machine learning models using partial least squares regression (PLSR) to rapidly predict genetic variation in photosynthetic and leaf traits across wheat populations, among other species. However, the application of published PLSR spectral models is limited by a fixed spectral wavelength range as input and the requirement of separate custom-built models for each trait and wavelength range. In addition, the use of reflectance spectra from the short-wave infrared region requires expensive multiple detector spectrometers. The ability to train a model that can accommodate input from different spectral ranges would potentially make such models extensible to more affordable sensors. Here we compare the accuracy of prediction of PLSR with various deep learning approaches and an ensemble model, each trained and tested using previously published data sets. RESULTS We demonstrate that the accuracy of PLSR to predict photosynthetic and related leaf traits in wheat can be improved with deep learning-based and ensemble models without overfitting. Additionally, these models can be flexibly applied across spectral ranges without significantly compromising accuracy. CONCLUSION The method reported provides an improved prediction of wheat leaf and photosynthetic traits from leaf hyperspectral reflectance and do not require a full range, high cost leaf spectrometer. We provide a web service for deploying these algorithms to predict physiological traits in wheat from a variety of spectral data sets, with important implications for wheat yield prediction and crop breeding.
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Affiliation(s)
- Robert T Furbank
- ARC Centre of Excellence for Translational Photosynthesis, Research School of Biology. Australian National University, Canberra, ACT, 2601, Australia.
| | - Viridiana Silva-Perez
- Agriculture Victoria, 110 Natimuk Road, Horsham, VIC, 3400, Australia
- CSIRO Agriculture and Food, PO Box 1700, Canberra, ACT, 2601, Australia
| | - John R Evans
- ARC Centre of Excellence for Translational Photosynthesis, Research School of Biology. Australian National University, Canberra, ACT, 2601, Australia
| | - Anthony G Condon
- CSIRO Agriculture and Food, PO Box 1700, Canberra, ACT, 2601, Australia
| | | | - Wennan He
- ARC Centre of Excellence for Translational Photosynthesis, Research School of Biology. Australian National University, Canberra, ACT, 2601, Australia
| | - Saul Newman
- ARC Centre of Excellence for Translational Photosynthesis, Research School of Biology. Australian National University, Canberra, ACT, 2601, Australia
| | - Richard Poiré
- Australian Plant Phenomics Facility, Australian National University, Canberra, ACT, 2601, Australia
| | - Ashley Hall
- Department of Computer Science and Computer Engineering, La Trobe University, Bundoora, VIC, 3086, Australia
| | - Zhen He
- Department of Computer Science and Computer Engineering, La Trobe University, Bundoora, VIC, 3086, Australia
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Incorporating Multi-Scale, Spectrally Detected Nitrogen Concentrations into Assessing Nitrogen Use Efficiency for Winter Wheat Breeding Populations. REMOTE SENSING 2021. [DOI: 10.3390/rs13193991] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Annually, over 100 million tons of nitrogen fertilizer are applied in wheat fields to ensure maximum productivity. This amount is often more than needed for optimal yield and can potentially have negative economic and environmental consequences. Monitoring crop nitrogen levels can inform managers of input requirements and potentially avoid excessive fertilization. Standard methods assessing plant nitrogen content, however, are time-consuming, destructive, and expensive. Therefore, the development of approaches estimating leaf nitrogen content in vivo and in situ could benefit fertilization management programs as well as breeding programs for nitrogen use efficiency (NUE). This study examined the ability of hyperspectral data to estimate leaf nitrogen concentrations and nitrogen uptake efficiency (NUpE) at the leaf and canopy levels in multiple winter wheat lines across two seasons. We collected spectral profiles of wheat foliage and canopies using full-range (350–2500 nm) spectroradiometers in combination with leaf tissue collection for standard analytical determination of nitrogen. We then applied partial least-squares regression, using spectral and reference nitrogen measurements, to build predictive models of leaf and canopy nitrogen concentrations. External validation of data from a multi-year model demonstrated effective nitrogen estimation at leaf and canopy level (R2 = 0.72, 0.67; root-mean-square error (RMSE) = 0.42, 0.46; normalized RMSE = 12, 13; bias = −0.06, 0.04, respectively). While NUpE was not directly well predicted using spectral data, NUpE values calculated from predicted leaf and canopy nitrogen levels were well correlated with NUpE determined using traditional methods, suggesting the potential of the approach in possibly replacing standard determination of plant nitrogen in assessing NUE. The results of our research reinforce the ability of hyperspectral data for the retrieval of nitrogen status and expand the utility of hyperspectral data in winter wheat lines to the application of nitrogen management practices and breeding programs.
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Yan Z, Guo Z, Serbin SP, Song G, Zhao Y, Chen Y, Wu S, Wang J, Wang X, Li J, Wang B, Wu Y, Su Y, Wang H, Rogers A, Liu L, Wu J. Spectroscopy outperforms leaf trait relationships for predicting photosynthetic capacity across different forest types. THE NEW PHYTOLOGIST 2021; 232:134-147. [PMID: 34165791 DOI: 10.1111/nph.17579] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Accepted: 06/20/2021] [Indexed: 06/13/2023]
Abstract
Leaf trait relationships are widely used to predict ecosystem function in terrestrial biosphere models (TBMs), in which leaf maximum carboxylation capacity (Vc,max ), an important trait for modelling photosynthesis, can be inferred from other easier-to-measure traits. However, whether trait-Vc,max relationships are robust across different forest types remains unclear. Here we used measurements of leaf traits, including one morphological trait (leaf mass per area), three biochemical traits (leaf water content, area-based leaf nitrogen content, and leaf chlorophyll content), one physiological trait (Vc,max ), as well as leaf reflectance spectra, and explored their relationships within and across three contrasting forest types in China. We found weak and forest type-specific relationships between Vc,max and the four morphological and biochemical traits (R2 ≤ 0.15), indicated by significantly changing slopes and intercepts across forest types. By contrast, reflectance spectroscopy effectively collapsed the differences in the trait-Vc,max relationships across three forest biomes into a single robust model for Vc,max (R2 = 0.77), and also accurately estimated the four traits (R2 = 0.75-0.94). These findings challenge the traditional use of the empirical trait-Vc,max relationships in TBMs for estimating terrestrial plant photosynthesis, but also highlight spectroscopy as an efficient alternative for characterising Vc,max and multitrait variability, with critical insights into ecosystem modelling and functional trait ecology.
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Affiliation(s)
- Zhengbing Yan
- Division for Ecology and Biodiversity, School of Biological Sciences, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Zhengfei Guo
- Division for Ecology and Biodiversity, School of Biological Sciences, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Shawn P Serbin
- Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, 11973, USA
| | - Guangqin Song
- Division for Ecology and Biodiversity, School of Biological Sciences, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Yingyi Zhao
- Division for Ecology and Biodiversity, School of Biological Sciences, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Yang Chen
- Division for Ecology and Biodiversity, School of Biological Sciences, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Shengbiao Wu
- Division for Ecology and Biodiversity, School of Biological Sciences, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Jing Wang
- Division for Ecology and Biodiversity, School of Biological Sciences, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Xin Wang
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing, 100093, China
| | - Jing Li
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing, 100093, China
- University of Chinese Academy of Sciences, Yuquanlu, Beijing, 100049, China
| | - Bin Wang
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing, 100093, China
- University of Chinese Academy of Sciences, Yuquanlu, Beijing, 100049, China
| | - Yuntao Wu
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing, 100093, China
- University of Chinese Academy of Sciences, Yuquanlu, Beijing, 100049, China
| | - Yanjun Su
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing, 100093, China
- University of Chinese Academy of Sciences, Yuquanlu, Beijing, 100049, China
| | - Han Wang
- Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China
- Joint Centre for Global Change Studies, Tsinghua University, Beijing, 100084, China
| | - Alistair Rogers
- Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, 11973, USA
| | - Lingli Liu
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing, 100093, China
- University of Chinese Academy of Sciences, Yuquanlu, Beijing, 100049, China
| | - Jin Wu
- Division for Ecology and Biodiversity, School of Biological Sciences, The University of Hong Kong, Pokfulam Road, Hong Kong, China
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Liu F, Song Q, Zhao J, Mao L, Bu H, Hu Y, Zhu XG. Canopy occupation volume as an indicator of canopy photosynthetic capacity. THE NEW PHYTOLOGIST 2021; 232:941-956. [PMID: 34245568 DOI: 10.1111/nph.17611] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 07/03/2021] [Indexed: 06/13/2023]
Abstract
Leaf angle and leaf area index together influence canopy light interception and canopy photosynthesis. However, so far, there is no effective method to identify the optimal combination of these two parameters for canopy photosynthesis. In this study, first a robust high-throughput method for accurate segmentation of maize organs based on 3D point clouds data was developed, then the segmented plant organs were used to generate new 3D point clouds for the canopy of altered architectures. With this, we simulated the synergistic effect of leaf area and leaf angle on canopy photosynthesis. The results show that, compared to the traditional parameters describing the canopy photosynthesis including leaf area index, facet angle and canopy coverage, a new parameter - the canopy occupation volume (COV) - can better explain the variations of canopy photosynthetic capacity. Specifically, COV can explain > 79% variations of canopy photosynthesis generated by changing leaf angle and > 84% variations of canopy photosynthesis generated by changing leaf area. As COV can be calculated in a high-throughput manner based on the canopy point clouds, it can be used to evaluate canopy architecture in breeding and agronomic research.
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Affiliation(s)
- Fusang Liu
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Qingfeng Song
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Jinke Zhao
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Linxiong Mao
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Hongyi Bu
- Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China
| | - Yong Hu
- Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China
| | - Xin-Guang Zhu
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 200031, China
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Burnett AC, Serbin SP, Davidson KJ, Ely KS, Rogers A. Detection of the metabolic response to drought stress using hyperspectral reflectance. JOURNAL OF EXPERIMENTAL BOTANY 2021; 72:6474-6489. [PMID: 34235536 DOI: 10.1093/jxb/erab255] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 06/02/2021] [Indexed: 06/13/2023]
Abstract
Drought is the most important limitation on crop yield. Understanding and detecting drought stress in crops is vital for improving water use efficiency through effective breeding and management. Leaf reflectance spectroscopy offers a rapid, non-destructive alternative to traditional techniques for measuring plant traits involved in a drought response. We measured drought stress in six glasshouse-grown agronomic species using physiological, biochemical, and spectral data. In contrast to physiological traits, leaf metabolite concentrations revealed drought stress before it was visible to the naked eye. We used full-spectrum leaf reflectance data to predict metabolite concentrations using partial least-squares regression, with validation R2 values of 0.49-0.87. We show for the first time that spectroscopy may be used for the quantitative estimation of proline and abscisic acid, demonstrating the first use of hyperspectral data to detect a phytohormone. We used linear discriminant analysis and partial least squares discriminant analysis to differentiate between watered plants and those subjected to drought based on measured traits (accuracy: 71%) and raw spectral data (66%). Finally, we validated our glasshouse-developed models in an independent field trial. We demonstrate that spectroscopy can detect drought stress via underlying biochemical changes, before visual differences occur, representing a powerful advance for measuring limitations on yield.
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Affiliation(s)
- Angela C Burnett
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
| | - Shawn P Serbin
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
| | - Kenneth J Davidson
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
| | - Kim S Ely
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
| | - Alistair Rogers
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
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45
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Burnett AC, Anderson J, Davidson KJ, Ely KS, Lamour J, Li Q, Morrison BD, Yang D, Rogers A, Serbin SP. A best-practice guide to predicting plant traits from leaf-level hyperspectral data using partial least squares regression. JOURNAL OF EXPERIMENTAL BOTANY 2021; 72:6175-6189. [PMID: 34131723 DOI: 10.1093/jxb/erab295] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 06/14/2021] [Indexed: 06/12/2023]
Abstract
Partial least squares regression (PLSR) modelling is a statistical technique for correlating datasets, and involves the fitting of a linear regression between two matrices. One application of PLSR enables leaf traits to be estimated from hyperspectral optical reflectance data, facilitating rapid, high-throughput, non-destructive plant phenotyping. This technique is of interest and importance in a wide range of contexts including crop breeding and ecosystem monitoring. The lack of a consensus in the literature on how to perform PLSR means that interpreting model results can be challenging, applying existing models to novel datasets can be impossible, and unknown or undisclosed assumptions can lead to incorrect or spurious predictions. We address this lack of consensus by proposing best practices for using PLSR to predict plant traits from leaf-level hyperspectral data, including a discussion of when PLSR is applicable, and recommendations for data collection. We provide a tutorial to demonstrate how to develop a PLSR model, in the form of an R script accompanying this manuscript. This practical guide will assist all those interpreting and using PLSR models to predict leaf traits from spectral data, and advocates for a unified approach to using PLSR for predicting traits from spectra in the plant sciences.
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Affiliation(s)
- Angela C Burnett
- Terrestrial Ecosystem Science and Technology Group, Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
| | - Jeremiah Anderson
- Terrestrial Ecosystem Science and Technology Group, Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
| | - Kenneth J Davidson
- Terrestrial Ecosystem Science and Technology Group, Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
| | - Kim S Ely
- Terrestrial Ecosystem Science and Technology Group, Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
| | - Julien Lamour
- Terrestrial Ecosystem Science and Technology Group, Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
| | - Qianyu Li
- Terrestrial Ecosystem Science and Technology Group, Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
| | - Bailey D Morrison
- Terrestrial Ecosystem Science and Technology Group, Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
| | - Dedi Yang
- Terrestrial Ecosystem Science and Technology Group, Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
| | - Alistair Rogers
- Terrestrial Ecosystem Science and Technology Group, Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
| | - Shawn P Serbin
- Terrestrial Ecosystem Science and Technology Group, Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
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46
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Joynson R, Molero G, Coombes B, Gardiner L, Rivera‐Amado C, Piñera‐Chávez FJ, Evans JR, Furbank RT, Reynolds MP, Hall A. Uncovering candidate genes involved in photosynthetic capacity using unexplored genetic variation in Spring Wheat. PLANT BIOTECHNOLOGY JOURNAL 2021; 19:1537-1552. [PMID: 33638599 PMCID: PMC8384606 DOI: 10.1111/pbi.13568] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 01/26/2021] [Indexed: 05/10/2023]
Abstract
To feed an ever-increasing population we must leverage advances in genomics and phenotyping to harness the variation in wheat breeding populations for traits like photosynthetic capacity which remains unoptimized. Here we survey a diverse set of wheat germplasm containing elite, introgression and synthetic derivative lines uncovering previously uncharacterized variation. We demonstrate how strategic integration of exotic material alleviates the D genome genetic bottleneck in wheat, increasing SNP rate by 62% largely due to Ae. tauschii synthetic wheat donors. Across the panel, 67% of the Ae. tauschii donor genome is represented as introgressions in elite backgrounds. We show how observed genetic variation together with hyperspectral reflectance data can be used to identify candidate genes for traits relating to photosynthetic capacity using association analysis. This demonstrates the value of genomic methods in uncovering hidden variation in wheat and how that variation can assist breeding efforts and increase our understanding of complex traits.
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Affiliation(s)
| | - Gemma Molero
- Global Wheat Program, International Maize and Wheat Improvement Centre (CIMMYT)TexcocoMexico
| | | | | | - Carolina Rivera‐Amado
- Global Wheat Program, International Maize and Wheat Improvement Centre (CIMMYT)TexcocoMexico
| | | | - John R. Evans
- ARC Centre of Excellence for Translational PhotosynthesisAustralian National UniversityCanberraAustralia
| | - Robert T. Furbank
- ARC Centre of Excellence for Translational PhotosynthesisAustralian National UniversityCanberraAustralia
| | - Matthew P. Reynolds
- Global Wheat Program, International Maize and Wheat Improvement Centre (CIMMYT)TexcocoMexico
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Burnett AC, Serbin SP, Rogers A. Source:sink imbalance detected with leaf- and canopy-level spectroscopy in a field-grown crop. PLANT, CELL & ENVIRONMENT 2021; 44:2466-2479. [PMID: 33764536 DOI: 10.1111/pce.14056] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 03/23/2021] [Accepted: 03/23/2021] [Indexed: 05/21/2023]
Abstract
The finely tuned balance between sources and sinks determines plant resource partitioning and regulates growth and development. Understanding and measuring metabolic indicators of source or sink limitation forms a vital part of global efforts to increase crop yield for future food security. We measured metabolic profiles of Cucurbita pepo (zucchini) grown in the field under carbon sink limitation and control conditions. We demonstrate that these profiles can be measured non-destructively using hyperspectral reflectance at both leaf and canopy scales. Total non-structural carbohydrates (TNC) increased 82% in sink-limited plants; leaf mass per unit area (LMA) increased 38% and free amino acids increased 22%. Partial least-squares regression (PLSR) models link these measured functional traits with reflectance data, enabling high-throughput estimation of traits comprising the sink limitation response. Leaf- and canopy-scale models for TNC had R2 values of 0.93 and 0.64 and %RMSE of 13 and 38%, respectively. For LMA, R2 values were 0.91 and 0.60 and %RMSE 7 and 14%; for free amino acids, R2 was 0.53 and 0.21 with %RMSE 20 and 26%. Remote sensing can enable accurate, rapid detection of sink limitation in the field at the leaf and canopy scale, greatly expanding our ability to understand and measure metabolic responses to stress.
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Affiliation(s)
- Angela C Burnett
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, New York, USA
| | - Shawn P Serbin
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, New York, USA
| | - Alistair Rogers
- Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, New York, USA
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48
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Grzybowski M, Wijewardane NK, Atefi A, Ge Y, Schnable JC. Hyperspectral reflectance-based phenotyping for quantitative genetics in crops: Progress and challenges. PLANT COMMUNICATIONS 2021; 2:100209. [PMID: 34327323 PMCID: PMC8299078 DOI: 10.1016/j.xplc.2021.100209] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 04/23/2021] [Accepted: 05/24/2021] [Indexed: 05/05/2023]
Abstract
Many biochemical and physiological properties of plants that are of interest to breeders and geneticists have extremely low throughput and/or can only be measured destructively. This has limited the use of information on natural variation in nutrient and metabolite abundance, as well as photosynthetic capacity in quantitative genetic contexts where it is necessary to collect data from hundreds or thousands of plants. A number of recent studies have demonstrated the potential to estimate many of these traits from hyperspectral reflectance data, primarily in ecophysiological contexts. Here, we summarize recent advances in the use of hyperspectral reflectance data for plant phenotyping, and discuss both the potential benefits and remaining challenges to its application in plant genetics contexts. The performances of previously published models in estimating six traits from hyperspectral reflectance data in maize were evaluated on new sample datasets, and the resulting predicted trait values shown to be heritable (e.g., explained by genetic factors) were estimated. The adoption of hyperspectral reflectance-based phenotyping beyond its current uses may accelerate the study of genes controlling natural variation in biochemical and physiological traits.
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Affiliation(s)
- Marcin Grzybowski
- Center for Plant Science Innovation and Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA
- Department of Plant Molecular Ecophysiology, Institute of Plant Experimental Biology and Biotechnology, Faculty of Biology, University of Warsaw, Warsaw, Poland
| | - Nuwan K. Wijewardane
- Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA
- Department of Agricultural Biological Engineering, Mississippi State University, Starkville, MS, USA
| | - Abbas Atefi
- Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Yufeng Ge
- Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - James C. Schnable
- Center for Plant Science Innovation and Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA
- Corresponding author
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Reynolds MP, Lewis JM, Ammar K, Basnet BR, Crespo-Herrera L, Crossa J, Dhugga KS, Dreisigacker S, Juliana P, Karwat H, Kishii M, Krause MR, Langridge P, Lashkari A, Mondal S, Payne T, Pequeno D, Pinto F, Sansaloni C, Schulthess U, Singh RP, Sonder K, Sukumaran S, Xiong W, Braun HJ. Harnessing translational research in wheat for climate resilience. JOURNAL OF EXPERIMENTAL BOTANY 2021; 72:5134-5157. [PMID: 34139769 PMCID: PMC8272565 DOI: 10.1093/jxb/erab256] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 06/14/2021] [Indexed: 05/24/2023]
Abstract
Despite being the world's most widely grown crop, research investments in wheat (Triticum aestivum and Triticum durum) fall behind those in other staple crops. Current yield gains will not meet 2050 needs, and climate stresses compound this challenge. However, there is good evidence that heat and drought resilience can be boosted through translating promising ideas into novel breeding technologies using powerful new tools in genetics and remote sensing, for example. Such technologies can also be applied to identify climate resilience traits from among the vast and largely untapped reserve of wheat genetic resources in collections worldwide. This review describes multi-pronged research opportunities at the focus of the Heat and Drought Wheat Improvement Consortium (coordinated by CIMMYT), which together create a pipeline to boost heat and drought resilience, specifically: improving crop design targets using big data approaches; developing phenomic tools for field-based screening and research; applying genomic technologies to elucidate the bases of climate resilience traits; and applying these outputs in developing next-generation breeding methods. The global impact of these outputs will be validated through the International Wheat Improvement Network, a global germplasm development and testing system that contributes key productivity traits to approximately half of the global wheat-growing area.
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Affiliation(s)
- Matthew P Reynolds
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Janet M Lewis
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Karim Ammar
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Bhoja R Basnet
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Kanwarpal S Dhugga
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - Philomin Juliana
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Hannes Karwat
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Masahiro Kishii
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Margaret R Krause
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Peter Langridge
- School of Agriculture, Food and Wine, University of Adelaide, Waite Campus, PMB1, Glen Osmond SA 5064, Australia
- Wheat Initiative, Julius Kühn-Institute, Königin-Luise-Str. 19, 14195 Berlin, Germany
| | - Azam Lashkari
- CIMMYT-Henan Collaborative Innovation Center, Henan Agricultural University, Zhengzhou, 450002, PR China
| | - Suchismita Mondal
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Thomas Payne
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Diego Pequeno
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Francisco Pinto
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Carolina Sansaloni
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Urs Schulthess
- CIMMYT-Henan Collaborative Innovation Center, Henan Agricultural University, Zhengzhou, 450002, PR China
| | - Ravi P Singh
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Kai Sonder
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - Wei Xiong
- CIMMYT-Henan Collaborative Innovation Center, Henan Agricultural University, Zhengzhou, 450002, PR China
| | - Hans J Braun
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
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Evaluation of RGB and Multispectral Unmanned Aerial Vehicle (UAV) Imagery for High-Throughput Phenotyping and Yield Prediction in Barley Breeding. REMOTE SENSING 2021. [DOI: 10.3390/rs13142670] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
With advances in plant genomics, plant phenotyping has become a new bottleneck in plant breeding and the need for reliable high-throughput plant phenotyping techniques has emerged. In the face of future climatic challenges, it does not seem appropriate to continue to solely select for grain yield and a few agronomically important traits. Therefore, new sensor-based high-throughput phenotyping has been increasingly used in plant breeding research, with the potential to provide non-destructive, objective and continuous plant characterization that reveals the formation of the final grain yield and provides insights into the physiology of the plant during the growth phase. In this context, we present the comparison of two sensor systems, Red-Green-Blue (RGB) and multispectral cameras, attached to unmanned aerial vehicles (UAV), and investigate their suitability for yield prediction using different modelling approaches in a segregating barley introgression population at three environments with weekly data collection during the entire vegetation period. In addition to vegetation indices, morphological traits such as canopy height, vegetation cover and growth dynamics traits were used for yield prediction. Repeatability analyses and genotype association studies of sensor-based traits were compared with reference values from ground-based phenotyping to test the use of conventional and new traits for barley breeding. The relative height estimation of the canopy by UAV achieved high precision (up to r = 0.93) and repeatability (up to R2 = 0.98). In addition, we found a great overlap of detected significant genotypes between the reference heights and sensor-based heights. The yield prediction accuracy of both sensor systems was at the same level and reached a maximum prediction accuracy of r2 = 0.82 with a continuous increase in precision throughout the entire vegetation period. Due to the lower costs and the consumer-friendly handling of image acquisition and processing, the RGB imagery seems to be more suitable for yield prediction in this study.
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