1
|
Taniguchi S, Sakamoto T, Nakamura H, Nonoue Y, Guan D, Fukuda A, Fukuda H, Wada KC, Ishii T, Yonemaru JI, Ogawa D. Phenology analysis for trait prediction using UAVs in a MAGIC rice population with different transplanting protocols. Front Artif Intell 2025; 7:1477637. [PMID: 39917549 PMCID: PMC11799559 DOI: 10.3389/frai.2024.1477637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2024] [Accepted: 12/23/2024] [Indexed: 02/09/2025] Open
Abstract
Unmanned aerial vehicles (UAVs) are one of the most effective tools for crop monitoring in the field. Time-series RGB and multispectral data obtained with UAVs can be used for revealing changes of three-dimensional growth. We previously showed using a rice population with our regular cultivation protocol that canopy height (CH) parameters extracted from time-series RGB data are useful for predicting manually measured traits such as days to heading (DTH), culm length (CL), and aboveground dried weight (ADW). However, whether CH parameters are applicable to other rice populations and to different cultivation methods, and whether vegetation indices such as the chlorophyll index green (CIg) can function for phenotype prediction remain to be elucidated. Here we show that CH and CIg exhibit different patterns with different cultivation protocols, and each has its own character for the prediction of rice phenotypes. We analyzed CH and CIg time-series data with a modified logistic model and a double logistic model, respectively, to extract individual parameters for each. The CH parameters were useful for predicting DTH, CL, ADW and stem and leaf weight (SLW) in a newly developed rice population under both regular and delayed cultivation protocols. The CIg parameters were also effective for predicting DTH and SLW, and could also be used to predict panicle weight (PW). The predictive ability worsened when different cultivation protocols were used, but this deterioration was mitigated by a calibration procedure using data from parental cultivars. These results indicate that the prediction of DTH, CL, ADW and SLW by CH parameters is robust to differences in rice populations and cultivation protocols, and that CIg parameters are an indispensable complement to the CH parameters for the predicting PW.
Collapse
Affiliation(s)
- Shoji Taniguchi
- Research Center for Agricultural Information Technology, National Agricultural and Food Research Organization (NARO), Tokyo, Japan
| | | | | | | | - Di Guan
- Institute of Crop Science, NARO, Tsukuba, Japan
| | | | | | | | | | - Jun-Ichi Yonemaru
- Research Center for Agricultural Information Technology, National Agricultural and Food Research Organization (NARO), Tokyo, Japan
| | | |
Collapse
|
2
|
Siangliw JL, Ruangsiri M, Theerawitaya C, Cha-um S, Poncheewin W, Songtoasesakul D, Thunnom B, Ruanjaichon V, Toojinda T. Contrasting Alleles of OsNRT1.1b Fostering Potential in Improving Nitrogen Use Efficiency in Rice. PLANTS (BASEL, SWITZERLAND) 2024; 13:2932. [PMID: 39458879 PMCID: PMC11510876 DOI: 10.3390/plants13202932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2024] [Revised: 10/15/2024] [Accepted: 10/15/2024] [Indexed: 10/28/2024]
Abstract
Nitrogen use efficiency (NUE) is important for the growth and development of rice and is significant in reducing the costs of rice production. OsNRT1.1b is involved in nitrate assimilation, and the alleles at position 21,759,092 on chromosome 10 clearly separate indica (Pathum Thani 1 (PTT1) and Homcholasit (HCS)) and japonica (Azucena and Leum Pua (LP)) rice varieties. Rice morphological and physiological traits were collected at three nitrogen levels (N0 = 0 kg ha-1, N7 = 43.75 kg ha-1, and N14 = 87.5 kg ha-1). Leaf and tiller numbers in PTT1 and HCS at N7 and N14 were two to three times higher than those at N0. At harvest, the biomass yield in PTT1 was the highest, while the total grain number in HCS was the maximum. The leaf widths and total chlorophyll contents (SPAD units) of Azucena and LP increased with nitrogen application as well as photosynthetic pigment parameters; for example, plant senescence reflectance indices (PSRIs), structure-insensitive pigment indices (SIPIs), and modified chlorophyll absorption ratio indices (MCARIs) were highly related in the japonica varieties. PTT1 and HCS, both carrying the A allele at OsNRT1.1b, had better NUE than Azucena and LP with the G allele. HCS, overall, had better NUE than PTT1. The translation to grain yield of assimilates was remarkable in PTT1 and HCS compared with Azucena and LP. In addition, HCS converted biomass for a 75% higher yield than PTT1. The ability of HCS to produce high yields was achieved even at N7 nitrogen fertilization, manifesting efficient use of nitrogen.
Collapse
Affiliation(s)
- Jonaliza L. Siangliw
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Khlong Luang, Pathum Thani 12120, Thailand; (M.R.); (C.T.); (S.C.-u.); (W.P.); (D.S.); (B.T.); (V.R.)
| | - Mathurada Ruangsiri
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Khlong Luang, Pathum Thani 12120, Thailand; (M.R.); (C.T.); (S.C.-u.); (W.P.); (D.S.); (B.T.); (V.R.)
| | - Cattarin Theerawitaya
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Khlong Luang, Pathum Thani 12120, Thailand; (M.R.); (C.T.); (S.C.-u.); (W.P.); (D.S.); (B.T.); (V.R.)
| | - Suriyan Cha-um
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Khlong Luang, Pathum Thani 12120, Thailand; (M.R.); (C.T.); (S.C.-u.); (W.P.); (D.S.); (B.T.); (V.R.)
| | - Wasin Poncheewin
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Khlong Luang, Pathum Thani 12120, Thailand; (M.R.); (C.T.); (S.C.-u.); (W.P.); (D.S.); (B.T.); (V.R.)
| | - Decha Songtoasesakul
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Khlong Luang, Pathum Thani 12120, Thailand; (M.R.); (C.T.); (S.C.-u.); (W.P.); (D.S.); (B.T.); (V.R.)
| | - Burin Thunnom
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Khlong Luang, Pathum Thani 12120, Thailand; (M.R.); (C.T.); (S.C.-u.); (W.P.); (D.S.); (B.T.); (V.R.)
| | - Vinitchan Ruanjaichon
- National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Khlong Luang, Pathum Thani 12120, Thailand; (M.R.); (C.T.); (S.C.-u.); (W.P.); (D.S.); (B.T.); (V.R.)
| | - Theerayut Toojinda
- Rice Science Center, Kasetsart University, Kamphangsaen, Nakhon Pathom 73140, Thailand;
| |
Collapse
|
3
|
Bernád V, Clarke JL, Negrão S. Editorial: Women in plant science - linking genome to phenome. FRONTIERS IN PLANT SCIENCE 2024; 15:1454686. [PMID: 39328798 PMCID: PMC11424543 DOI: 10.3389/fpls.2024.1454686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Accepted: 08/27/2024] [Indexed: 09/28/2024]
Affiliation(s)
- Villő Bernád
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| | - Jennifer L. Clarke
- International Plant Phenotyping Network, Institute for Bio- and Geosciences, Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
- Department of Food Science and Technology, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Sónia Negrão
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| |
Collapse
|
4
|
Okada M, Barras C, Toda Y, Hamazaki K, Ohmori Y, Yamasaki Y, Takahashi H, Takanashi H, Tsuda M, Hirai MY, Tsujimoto H, Kaga A, Nakazono M, Fujiwara T, Iwata H. High-Throughput Phenotyping of Soybean Biomass: Conventional Trait Estimation and Novel Latent Feature Extraction Using UAV Remote Sensing and Deep Learning Models. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0244. [PMID: 39252878 PMCID: PMC11382017 DOI: 10.34133/plantphenomics.0244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2024] [Accepted: 08/11/2024] [Indexed: 09/11/2024]
Abstract
High-throughput phenotyping serves as a framework to reduce chronological costs and accelerate breeding cycles. In this study, we developed models to estimate the phenotypes of biomass-related traits in soybean (Glycine max) using unmanned aerial vehicle (UAV) remote sensing and deep learning models. In 2018, a field experiment was conducted using 198 soybean germplasm accessions with known whole-genome sequences under 2 irrigation conditions: drought and control. We used a convolutional neural network (CNN) as a model to estimate the phenotypic values of 5 conventional biomass-related traits: dry weight, main stem length, numbers of nodes and branches, and plant height. We utilized manually measured phenotypes of conventional traits along with RGB images and digital surface models from UAV remote sensing to train our CNN models. The accuracy of the developed models was assessed through 10-fold cross-validation, which demonstrated their ability to accurately estimate the phenotypes of all conventional traits simultaneously. Deep learning enabled us to extract features that exhibited strong correlations with the output (i.e., phenotypes of the target traits) and accurately estimate the values of the features from the input data. We considered the extracted low-dimensional features as phenotypes in the latent space and attempted to annotate them based on the phenotypes of conventional traits. Furthermore, we validated whether these low-dimensional latent features were genetically controlled by assessing the accuracy of genomic predictions. The results revealed the potential utility of these low-dimensional latent features in actual breeding scenarios.
Collapse
Affiliation(s)
- Mashiro Okada
- Graduated School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
| | - Clément Barras
- Graduated School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
| | - Yusuke Toda
- Graduated School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
| | - Kosuke Hamazaki
- Center for Advanced Intelligence Project, RIKEN, Kashiwa, Chiba, Japan
| | - Yoshihiro Ohmori
- Graduated School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
| | - Yuji Yamasaki
- Arid Land Research Center, Tottori University, Tottori, Japan
| | - Hirokazu Takahashi
- Graduated School of Bioagricultural Sciences, Nagoya University, Nagoya, Japan
| | - Hideki Takanashi
- Graduated School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
| | - Mai Tsuda
- Faculty of Food and Nutritional Sciences, Toyo University, Saitama, Japan
| | | | | | - Akito Kaga
- Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Japan
| | - Mikio Nakazono
- Graduated School of Bioagricultural Sciences, Nagoya University, Nagoya, Japan
| | - Toru Fujiwara
- Graduated School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
| | - Hiroyoshi Iwata
- Graduated School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
| |
Collapse
|
5
|
Kulhánek M, Asrade DA, Suran P, Sedlář O, Černý J, Balík J. Plant Nutrition-New Methods Based on the Lessons of History: A Review. PLANTS (BASEL, SWITZERLAND) 2023; 12:4150. [PMID: 38140480 PMCID: PMC10747035 DOI: 10.3390/plants12244150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 12/08/2023] [Accepted: 12/10/2023] [Indexed: 12/24/2023]
Abstract
As with new technologies, plant nutrition has taken a big step forward in the last two decades. The main objective of this review is to briefly summarise the main pathways in modern plant nutrition and attract potential researchers and publishers to this area. First, this review highlights the importance of long-term field experiments, which provide us with valuable information about the effects of different applied strategies. The second part is dedicated to the new analytical technologies (tomography, spectrometry, and chromatography), intensively studied environments (rhizosphere, soil microbial communities, and enzymatic activity), nutrient relationship indexes, and the general importance of proper data evaluation. The third section is dedicated to the strategies of plant nutrition, i.e., (i) plant breeding, (ii) precision farming, (iii) fertiliser placement, (iv) biostimulants, (v) waste materials as a source of nutrients, and (vi) nanotechnologies. Finally, the increasing environmental risks related to plant nutrition, including biotic and abiotic stress, mainly the threat of soil salinity, are mentioned. In the 21st century, fertiliser application trends should be shifted to local application, precise farming, and nanotechnology; amended with ecofriendly organic fertilisers to ensure sustainable agricultural practices; and supported by new, highly effective crop varieties. To optimise agriculture, only the combination of the mentioned modern strategies supported by a proper analysis based on long-term observations seems to be a suitable pathway.
Collapse
Affiliation(s)
- Martin Kulhánek
- Department of Agro-Environmental Chemistry and Plant Nutrition, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences, 165 00 Prague, Czech Republic; (D.A.A.); (P.S.); (O.S.); (J.Č.); (J.B.)
| | | | | | | | | | | |
Collapse
|
6
|
Anshori MF, Dirpan A, Sitaresmi T, Rossi R, Farid M, Hairmansis A, Sapta Purwoko B, Suwarno WB, Nugraha Y. An overview of image-based phenotyping as an adaptive 4.0 technology for studying plant abiotic stress: A bibliometric and literature review. Heliyon 2023; 9:e21650. [PMID: 38027954 PMCID: PMC10660044 DOI: 10.1016/j.heliyon.2023.e21650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 09/20/2023] [Accepted: 10/25/2023] [Indexed: 12/01/2023] Open
Abstract
Improving the tolerance of crop species to abiotic stresses that limit plant growth and productivity is essential for mitigating the emerging problems of global warming. In this context, imaged data analysis represents an effective method in the 4.0 technology era, where this method has the non-destructive and recursive characterization of plant phenotypic traits as selection criteria. So, the plant breeders are helped in the development of adapted and climate-resilient crop varieties. Although image-based phenotyping has recently resulted in remarkable improvements for identifying the crop status under a range of growing conditions, the topic of its application for assessing the plant behavioral responses to abiotic stressors has not yet been extensively reviewed. For such a purpose, bibliometric analysis is an ideal analytical concept to analyze the evolution and interplay of image-based phenotyping to abiotic stresses by objectively reviewing the literature in light of existing database. Bibliometricy, a bibliometric analysis was applied using a systematic methodology which involved data mining, mining data improvement and analysis, and manuscript construction. The obtained results indicate that there are 554 documents related to image-based phenotyping to abiotic stress until 5 January 2023. All document showed the future development trends of image-based phenotyping will be mainly centered in the United States, European continent and China. The keywords analysis major focus to the application of 4.0 technology and machine learning in plant breeding, especially to create the tolerant variety under abiotic stresses. Drought and saline become an abiotic stress often using image-based phenotyping. Besides that, the rice, wheat and maize as the main commodities in this topic. In conclusion, the present work provides information on resolutive interactions in developing image-based phenotyping to abiotic stress, especially optimizing high-throughput sensors in image-based phenotyping for the future development.
Collapse
Affiliation(s)
| | - Andi Dirpan
- Department of Agricultural Technology, Hasanuddin University, Makassar, 90245, Indonesia
- Center of Excellence in Science and Technology on Food Product Diversification, 90245, Makassar, Indonesia
| | - Trias Sitaresmi
- Research Center for Food Crops, Research Organization for Agriculture and Food, National Research and Innovation Agency, 16911, Cibinong, Indonesia
| | - Riccardo Rossi
- Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence (UNIFI), Piazzale delle Cascine 18, 50144, Florence, Italy
| | - Muh Farid
- Department of Agronomy, Hasanuddin University, Makassar, 90245, Indonesia
| | - Aris Hairmansis
- Research Center for Food Crops, Research Organization for Agriculture and Food, National Research and Innovation Agency, 16911, Cibinong, Indonesia
| | - Bambang Sapta Purwoko
- Department of Agronomy and Horticulture, Faculty of Agriculture, IPB University, Bogor, 11680, Indonesia
| | - Willy Bayuardi Suwarno
- Department of Agronomy and Horticulture, Faculty of Agriculture, IPB University, Bogor, 11680, Indonesia
| | - Yudhistira Nugraha
- Research Center for Food Crops, Research Organization for Agriculture and Food, National Research and Innovation Agency, 16911, Cibinong, Indonesia
| |
Collapse
|
7
|
Cudjoe DK, Virlet N, Castle M, Riche AB, Mhada M, Waine TW, Mohareb F, Hawkesford MJ. Field phenotyping for African crops: overview and perspectives. FRONTIERS IN PLANT SCIENCE 2023; 14:1219673. [PMID: 37860243 PMCID: PMC10582954 DOI: 10.3389/fpls.2023.1219673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 09/07/2023] [Indexed: 10/21/2023]
Abstract
Improvements in crop productivity are required to meet the dietary demands of the rapidly-increasing African population. The development of key staple crop cultivars that are high-yielding and resilient to biotic and abiotic stresses is essential. To contribute to this objective, high-throughput plant phenotyping approaches are important enablers for the African plant science community to measure complex quantitative phenotypes and to establish the genetic basis of agriculturally relevant traits. These advances will facilitate the screening of germplasm for optimum performance and adaptation to low-input agriculture and resource-constrained environments. Increasing the capacity to investigate plant function and structure through non-invasive technologies is an effective strategy to aid plant breeding and additionally may contribute to precision agriculture. However, despite the significant global advances in basic knowledge and sensor technology for plant phenotyping, Africa still lags behind in the development and implementation of these systems due to several practical, financial, geographical and political barriers. Currently, field phenotyping is mostly carried out by manual methods that are prone to error, costly, labor-intensive and may come with adverse economic implications. Therefore, improvements in advanced field phenotyping capabilities and appropriate implementation are key factors for success in modern breeding and agricultural monitoring. In this review, we provide an overview of the current state of field phenotyping and the challenges limiting its implementation in some African countries. We suggest that the lack of appropriate field phenotyping infrastructures is impeding the development of improved crop cultivars and will have a detrimental impact on the agricultural sector and on food security. We highlight the prospects for integrating emerging and advanced low-cost phenotyping technologies into breeding protocols and characterizing crop responses to environmental challenges in field experimentation. Finally, we explore strategies for overcoming the barriers and maximizing the full potential of emerging field phenotyping technologies in African agriculture. This review paper will open new windows and provide new perspectives for breeders and the entire plant science community in Africa.
Collapse
Affiliation(s)
- Daniel K. Cudjoe
- Sustainable Soils and Crops, Rothamsted Research, Harpenden, United Kingdom
- School of Water, Energy and Environment, Cranfield University, Cranfield, Bedfordshire, United Kingdom
| | - Nicolas Virlet
- Sustainable Soils and Crops, Rothamsted Research, Harpenden, United Kingdom
| | - March Castle
- Sustainable Soils and Crops, Rothamsted Research, Harpenden, United Kingdom
| | - Andrew B. Riche
- Sustainable Soils and Crops, Rothamsted Research, Harpenden, United Kingdom
| | - Manal Mhada
- AgroBiosciences Department, Mohammed VI Polytechnic University (UM6P), Benguérir, Morocco
| | - Toby W. Waine
- School of Water, Energy and Environment, Cranfield University, Cranfield, Bedfordshire, United Kingdom
| | - Fady Mohareb
- School of Water, Energy and Environment, Cranfield University, Cranfield, Bedfordshire, United Kingdom
| | | |
Collapse
|
8
|
Zhang P, Huang J, Ma Y, Wang X, Kang M, Song Y. Crop/Plant Modeling Supports Plant Breeding: II. Guidance of Functional Plant Phenotyping for Trait Discovery. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0091. [PMID: 37780969 PMCID: PMC10538623 DOI: 10.34133/plantphenomics.0091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 08/26/2023] [Indexed: 10/03/2023]
Abstract
Observable morphological traits are widely employed in plant phenotyping for breeding use, which are often the external phenotypes driven by a chain of functional actions in plants. Identifying and phenotyping inherently functional traits for crop improvement toward high yields or adaptation to harsh environments remains a major challenge. Prediction of whole-plant performance in functional-structural plant models (FSPMs) is driven by plant growth algorithms based on organ scale wrapped up with micro-environments. In particular, the models are flexible for scaling down or up through specific functions at the organ nexus, allowing the prediction of crop system behaviors from the genome to the field. As such, by virtue of FSPMs, model parameters that determine organogenesis, development, biomass production, allocation, and morphogenesis from a molecular to the whole plant level can be profiled systematically and made readily available for phenotyping. FSPMs can provide rich functional traits representing biological regulatory mechanisms at various scales in a dynamic system, e.g., Rubisco carboxylation rate, mesophyll conductance, specific leaf nitrogen, radiation use efficiency, and source-sink ratio apart from morphological traits. High-throughput phenotyping such traits is also discussed, which provides an unprecedented opportunity to evolve FSPMs. This will accelerate the co-evolution of FSPMs and plant phenomics, and thus improving breeding efficiency. To expand the great promise of FSPMs in crop science, FSPMs still need more effort in multiscale, mechanistic, reproductive organ, and root system modeling. In summary, this study demonstrates that FSPMs are invaluable tools in guiding functional trait phenotyping at various scales and can thus provide abundant functional targets for phenotyping toward crop improvement.
Collapse
Affiliation(s)
- Pengpeng Zhang
- School of Agronomy, Anhui Agricultural University, Hefei, Anhui Province 230036, China
| | - Jingyao Huang
- School of Agronomy, Anhui Agricultural University, Hefei, Anhui Province 230036, China
| | - Yuntao Ma
- College of Land Science and Technology, China Agricultural University, Beijing 100094, China
| | - Xiujuan Wang
- The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Mengzhen Kang
- The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Youhong Song
- School of Agronomy, Anhui Agricultural University, Hefei, Anhui Province 230036, China
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD 4350, Australia
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD 4350, Australia
| |
Collapse
|
9
|
Song Q, Liu F, Bu H, Zhu XG. Quantifying Contributions of Different Factors to Canopy Photosynthesis in 2 Maize Varieties: Development of a Novel 3D Canopy Modeling Pipeline. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0075. [PMID: 37502446 PMCID: PMC10371248 DOI: 10.34133/plantphenomics.0075] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Accepted: 07/01/2023] [Indexed: 07/29/2023]
Abstract
Crop yield potential is intrinsically related to canopy photosynthesis; therefore, improving canopy photosynthetic efficiency is a major focus of current efforts to enhance crop yield. Canopy photosynthesis rate (Ac) is influenced by several factors, including plant architecture, leaf chlorophyll content, and leaf photosynthetic properties, which interact with each other. Identifying factors that restrict canopy photosynthesis and target adjustments to improve canopy photosynthesis in a specific crop cultivar pose an important challenge for the breeding community. To address this challenge, we developed a novel pipeline that utilizes factorial analysis, canopy photosynthesis modeling, and phenomics data collected using a 64-camera multi-view stereo system, enabling the dissection of the contributions of different factors to differences in canopy photosynthesis between maize cultivars. We applied this method to 2 maize varieties, W64A and A619, and found that leaf photosynthetic efficiency is the primary determinant (17.5% to 29.2%) of the difference in Ac between 2 maize varieties at all stages, and plant architecture at early stages also contribute to the difference in Ac (5.3% to 6.7%). Additionally, the contributions of each leaf photosynthetic parameter and plant architectural trait were dissected. We also found that the leaf photosynthetic parameters were linearly correlated with Ac and plant architecture traits were non-linearly related to Ac. This study developed a novel pipeline that provides a method for dissecting the relationship among individual phenotypes controlling the complex trait of canopy photosynthesis.
Collapse
Affiliation(s)
- 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 200032, China
| | - 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 200032, China
| | - Hongyi Bu
- Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 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 200032, China
| |
Collapse
|
10
|
Montanaro G, Petrozza A, Rustioni L, Cellini F, Nuzzo V. Phenotyping Key Fruit Quality Traits in Olive Using RGB Images and Back Propagation Neural Networks. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0061. [PMID: 37363144 PMCID: PMC10289815 DOI: 10.34133/plantphenomics.0061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 06/06/2023] [Indexed: 06/28/2023]
Abstract
To predict oil and phenol concentrations in olive fruit, the combination of back propagation neural networks (BPNNs) and contact-less plant phenotyping techniques was employed to retrieve RGB image-based digital proxies of oil and phenol concentrations. Fruits of cultivars (×3) differing in ripening time were sampled (~10-day interval, ×2 years), pictured and analyzed for phenol and oil concentrations. Prior to this, fruit samples were pictured and images were segmented to extract the red (R), green (G), and blue (B) mean pixel values that were rearranged in 35 RGB-based colorimetric indexes. Three BPNNs were designed using as input variables (a) the original 35 RGB indexes, (b) the scores of principal components after a principal component analysis (PCA) pre-processing of those indexes, and (c) a reduced number (28) of the RGB indexes achieved after a sparse PCA. The results show that the predictions reached the highest mean R2 values ranging from 0.87 to 0.95 (oil) and from 0.81 to 0.90 (phenols) across the BPNNs. In addition to the R2, other performance metrics were calculated (root mean squared error and mean absolute error) and combined into a general performance indicator (GPI). The resulting rank of the GPI suggests that a BPNN with a specific topology might be designed for cultivars grouped according to their ripening period. The present study documented that an RGB-based image phenotyping can effectively predict key quality traits in olive fruit supporting the developing olive sector within a digital agriculture domain.
Collapse
Affiliation(s)
| | - Angelo Petrozza
- ALSIA, Agenzia Lucana Sviluppo Innovazione in Agricoltura, Metapontum Agrobios Research Center, 75010 Metaponto, Italy
| | - Laura Rustioni
- Department of Biological and Environmental Sciences and Technologies, University of Salento, Lecce, Italy
| | - Francesco Cellini
- ALSIA, Agenzia Lucana Sviluppo Innovazione in Agricoltura, Metapontum Agrobios Research Center, 75010 Metaponto, Italy
| | - Vitale Nuzzo
- Università degli Studi della Basilicata, 85100 Potenza, Italy
| |
Collapse
|
11
|
Chen J, Zhou J, Li Q, Li H, Xia Y, Jackson R, Sun G, Zhou G, Deakin G, Jiang D, Zhou J. CropQuant-Air: an AI-powered system to enable phenotypic analysis of yield- and performance-related traits using wheat canopy imagery collected by low-cost drones. FRONTIERS IN PLANT SCIENCE 2023; 14:1219983. [PMID: 37404534 PMCID: PMC10316027 DOI: 10.3389/fpls.2023.1219983] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 05/26/2023] [Indexed: 07/06/2023]
Abstract
As one of the most consumed stable foods around the world, wheat plays a crucial role in ensuring global food security. The ability to quantify key yield components under complex field conditions can help breeders and researchers assess wheat's yield performance effectively. Nevertheless, it is still challenging to conduct large-scale phenotyping to analyse canopy-level wheat spikes and relevant performance traits, in the field and in an automated manner. Here, we present CropQuant-Air, an AI-powered software system that combines state-of-the-art deep learning (DL) models and image processing algorithms to enable the detection of wheat spikes and phenotypic analysis using wheat canopy images acquired by low-cost drones. The system includes the YOLACT-Plot model for plot segmentation, an optimised YOLOv7 model for quantifying the spike number per m2 (SNpM2) trait, and performance-related trait analysis using spectral and texture features at the canopy level. Besides using our labelled dataset for model training, we also employed the Global Wheat Head Detection dataset to incorporate varietal features into the DL models, facilitating us to perform reliable yield-based analysis from hundreds of varieties selected from main wheat production regions in China. Finally, we employed the SNpM2 and performance traits to develop a yield classification model using the Extreme Gradient Boosting (XGBoost) ensemble and obtained significant positive correlations between the computational analysis results and manual scoring, indicating the reliability of CropQuant-Air. To ensure that our work could reach wider researchers, we created a graphical user interface for CropQuant-Air, so that non-expert users could readily use our work. We believe that our work represents valuable advances in yield-based field phenotyping and phenotypic analysis, providing useful and reliable toolkits to enable breeders, researchers, growers, and farmers to assess crop-yield performance in a cost-effective approach.
Collapse
Affiliation(s)
- Jiawei Chen
- State Key Laboratory of Crop Genetics & Germplasm Enhancement, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, China
- College of Engineering, Nanjing Agricultural University, Nanjing, China
| | - Jie Zhou
- State Key Laboratory of Crop Genetics & Germplasm Enhancement, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, China
- College of Engineering, Nanjing Agricultural University, Nanjing, China
| | - Qing Li
- Regional Technique Innovation Center for Wheat Production, Key Laboratory of Crop Physiology and Ecology in Southern China, Ministry of Agriculture, Nanjing Agricultural University, Nanjing, China
| | - Hanghang Li
- State Key Laboratory of Crop Genetics & Germplasm Enhancement, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, China
| | - Yunpeng Xia
- State Key Laboratory of Crop Genetics & Germplasm Enhancement, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, China
| | - Robert Jackson
- Cambridge Crop Research, National Institute of Agricultural Botany (NIAB), Cambridge, United Kingdom
| | - Gang Sun
- State Key Laboratory of Crop Genetics & Germplasm Enhancement, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, China
| | - Guodong Zhou
- State Key Laboratory of Crop Genetics & Germplasm Enhancement, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, China
| | - Greg Deakin
- Cambridge Crop Research, National Institute of Agricultural Botany (NIAB), Cambridge, United Kingdom
| | - Dong Jiang
- Regional Technique Innovation Center for Wheat Production, Key Laboratory of Crop Physiology and Ecology in Southern China, Ministry of Agriculture, Nanjing Agricultural University, Nanjing, China
| | - Ji Zhou
- State Key Laboratory of Crop Genetics & Germplasm Enhancement, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, China
- Cambridge Crop Research, National Institute of Agricultural Botany (NIAB), Cambridge, United Kingdom
| |
Collapse
|
12
|
Roychowdhury R, Das SP, Gupta A, Parihar P, Chandrasekhar K, Sarker U, Kumar A, Ramrao DP, Sudhakar C. Multi-Omics Pipeline and Omics-Integration Approach to Decipher Plant's Abiotic Stress Tolerance Responses. Genes (Basel) 2023; 14:1281. [PMID: 37372461 PMCID: PMC10298225 DOI: 10.3390/genes14061281] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 06/03/2023] [Accepted: 06/14/2023] [Indexed: 06/29/2023] Open
Abstract
The present day's ongoing global warming and climate change adversely affect plants through imposing environmental (abiotic) stresses and disease pressure. The major abiotic factors such as drought, heat, cold, salinity, etc., hamper a plant's innate growth and development, resulting in reduced yield and quality, with the possibility of undesired traits. In the 21st century, the advent of high-throughput sequencing tools, state-of-the-art biotechnological techniques and bioinformatic analyzing pipelines led to the easy characterization of plant traits for abiotic stress response and tolerance mechanisms by applying the 'omics' toolbox. Panomics pipeline including genomics, transcriptomics, proteomics, metabolomics, epigenomics, proteogenomics, interactomics, ionomics, phenomics, etc., have become very handy nowadays. This is important to produce climate-smart future crops with a proper understanding of the molecular mechanisms of abiotic stress responses by the plant's genes, transcripts, proteins, epigenome, cellular metabolic circuits and resultant phenotype. Instead of mono-omics, two or more (hence 'multi-omics') integrated-omics approaches can decipher the plant's abiotic stress tolerance response very well. Multi-omics-characterized plants can be used as potent genetic resources to incorporate into the future breeding program. For the practical utility of crop improvement, multi-omics approaches for particular abiotic stress tolerance can be combined with genome-assisted breeding (GAB) by being pyramided with improved crop yield, food quality and associated agronomic traits and can open a new era of omics-assisted breeding. Thus, multi-omics pipelines together are able to decipher molecular processes, biomarkers, targets for genetic engineering, regulatory networks and precision agriculture solutions for a crop's variable abiotic stress tolerance to ensure food security under changing environmental circumstances.
Collapse
Affiliation(s)
- Rajib Roychowdhury
- Department of Plant Pathology and Weed Research, Institute of Plant Protection, Agricultural Research Organization (ARO)—The Volcani Institute, Rishon Lezion 7505101, Israel
| | - Soumya Prakash Das
- School of Bioscience, Seacom Skills University, Bolpur 731236, West Bengal, India
| | - Amber Gupta
- Dr. Vikram Sarabhai Institute of Cell and Molecular Biology, Faculty of Science, Maharaja Sayajirao University of Baroda, Vadodara 390002, Gujarat, India
| | - Parul Parihar
- Department of Biotechnology and Bioscience, Banasthali Vidyapith, Banasthali 304022, Rajasthan, India
| | - Kottakota Chandrasekhar
- Department of Plant Biochemistry and Biotechnology, Sri Krishnadevaraya College of Agricultural Sciences (SKCAS), Affiliated to Acharya N.G. Ranga Agricultural University (ANGRAU), Guntur 522034, Andhra Pradesh, India
| | - Umakanta Sarker
- Department of Genetics and Plant Breeding, Faculty of Agriculture, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh
| | - Ajay Kumar
- Department of Botany, Maharshi Vishwamitra (M.V.) College, Buxar 802102, Bihar, India
| | - Devade Pandurang Ramrao
- Department of Biotechnology, Mizoram University, Pachhunga University College Campus, Aizawl 796001, Mizoram, India
| | - Chinta Sudhakar
- Plant Molecular Biology Laboratory, Department of Botany, Sri Krishnadevaraya University, Anantapur 515003, Andhra Pradesh, India
| |
Collapse
|
13
|
Zhang J, Wang X, Liu J, Zhang D, Lu Y, Zhou Y, Sun L, Hou S, Fan X, Shen S, Zhao J. Multispectral Drone Imagery and SRGAN for Rapid Phenotypic Mapping of Individual Chinese Cabbage Plants. PLANT PHENOMICS (WASHINGTON, D.C.) 2022; 2022:0007. [PMID: 37266137 PMCID: PMC10230957 DOI: 10.34133/plantphenomics.0007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Accepted: 11/07/2022] [Indexed: 06/03/2023]
Abstract
The phenotypic parameters of crop plants can be evaluated accurately and quickly using an unmanned aerial vehicle (UAV) equipped with imaging equipment. In this study, hundreds of images of Chinese cabbage (Brassica rapa L. ssp. pekinensis) germplasm resources were collected with a low-cost UAV system and used to estimate cabbage width, length, and relative chlorophyll content (soil plant analysis development [SPAD] value). The super-resolution generative adversarial network (SRGAN) was used to improve the resolution of the original image, and the semantic segmentation network Unity Networking (UNet) was used to process images for the segmentation of each individual Chinese cabbage. Finally, the actual length and width were calculated on the basis of the pixel value of the individual cabbage and the ground sampling distance. The SPAD value of Chinese cabbage was also analyzed on the basis of an RGB image of a single cabbage after background removal. After comparison of various models, the model in which visible images were enhanced with SRGAN showed the best performance. With the validation set and the UNet model, the segmentation accuracy was 94.43%. For Chinese cabbage dimensions, the model was better at estimating length than width. The R2 of the visible-band model with images enhanced using SRGAN was greater than 0.84. For SPAD prediction, the R2 of the model with images enhanced with SRGAN was greater than 0.78. The root mean square errors of the 3 semantic segmentation network models were all less than 2.18. The results showed that the width, length, and SPAD value of Chinese cabbage predicted using UAV imaging were comparable to those obtained from manual measurements in the field. Overall, this research demonstrates not only that UAVs are useful for acquiring quantitative phenotypic data on Chinese cabbage but also that a regression model can provide reliable SPAD predictions. This approach offers a reliable and convenient phenotyping tool for the investigation of Chinese cabbage breeding traits.
Collapse
Affiliation(s)
- Jun Zhang
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, 071000 Baoding, China
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, 071000 Baoding, China
| | - Xinxin Wang
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, 071000 Baoding, China
- Mountain Area Research Institute, Hebei Agricultural University, 071001 Baoding, China
| | - Jingyan Liu
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, 071000 Baoding, China
| | - Dongfang Zhang
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, 071000 Baoding, China
- College of Horticulture, Hebei Agricultural University, 071000 Baoding, China
| | - Yin Lu
- College of Horticulture, Hebei Agricultural University, 071000 Baoding, China
| | - Yuhong Zhou
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, 071000 Baoding, China
| | - Lei Sun
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, 071000 Baoding, China
| | - Shenglin Hou
- Hebei Academy of Agriculture and Forestry Sciences, 050000 Shijiazhuang, China
| | - Xiaofei Fan
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, 071000 Baoding, China
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, 071000 Baoding, China
| | - Shuxing Shen
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, 071000 Baoding, China
- College of Horticulture, Hebei Agricultural University, 071000 Baoding, China
| | - Jianjun Zhao
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, 071000 Baoding, China
- College of Horticulture, Hebei Agricultural University, 071000 Baoding, China
| |
Collapse
|
14
|
Taniguchi S, Sakamoto T, Imase R, Nonoue Y, Tsunematsu H, Goto A, Matsushita K, Ohmori S, Maeda H, Takeuchi Y, Ishii T, Yonemaru JI, Ogawa D. Prediction of heading date, culm length, and biomass from canopy-height-related parameters derived from time-series UAV observations of rice. FRONTIERS IN PLANT SCIENCE 2022; 13:998803. [PMID: 36582650 PMCID: PMC9792801 DOI: 10.3389/fpls.2022.998803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 11/28/2022] [Indexed: 06/17/2023]
Abstract
Unmanned aerial vehicles (UAVs) are powerful tools for monitoring crops for high-throughput phenotyping. Time-series aerial photography of fields can record the whole process of crop growth. Canopy height (CH), which is vertical plant growth, has been used as an indicator for the evaluation of lodging tolerance and the prediction of biomass and yield. However, there have been few attempts to use UAV-derived time-series CH data for field testing of crop lines. Here we provide a novel framework for trait prediction using CH data in rice. We generated UAV-based digital surface models of crops to extract CH data of 30 Japanese rice cultivars in 2019, 2020, and 2021. CH-related parameters were calculated in a non-linear time-series model as an S-shaped plant growth curve. The maximum saturation CH value was the most important predictor for culm length. The time point at the maximum CH contributed to the prediction of days to heading, and was able to predict stem and leaf weight and aboveground weight, possibly reflecting the association of biomass with duration of vegetative growth. These results indicate that the CH-related parameters acquired by UAV can be useful as predictors of traits typically measured by hand.
Collapse
Affiliation(s)
- Shoji Taniguchi
- Research Center for Agricultural Information Technology, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Toshihiro Sakamoto
- Institute for Agro-Environmental Sciences, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Ryoji Imase
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Yasunori Nonoue
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Hiroshi Tsunematsu
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Akitoshi Goto
- Research Center for Agricultural Information Technology, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Kei Matsushita
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Sinnosuke Ohmori
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Hideo Maeda
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Yoshinobu Takeuchi
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Takuro Ishii
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Jun-ichi Yonemaru
- Research Center for Agricultural Information Technology, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| | - Daisuke Ogawa
- Institute of Crop Science, National Agricultural and Food Research Organization (NARO), Tsukuba, Japan
| |
Collapse
|
15
|
Villalobos-López MA, Arroyo-Becerra A, Quintero-Jiménez A, Iturriaga G. Biotechnological Advances to Improve Abiotic Stress Tolerance in Crops. Int J Mol Sci 2022; 23:12053. [PMID: 36233352 PMCID: PMC9570234 DOI: 10.3390/ijms231912053] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/02/2022] [Accepted: 10/06/2022] [Indexed: 11/16/2022] Open
Abstract
The major challenges that agriculture is facing in the twenty-first century are increasing droughts, water scarcity, flooding, poorer soils, and extreme temperatures due to climate change. However, most crops are not tolerant to extreme climatic environments. The aim in the near future, in a world with hunger and an increasing population, is to breed and/or engineer crops to tolerate abiotic stress with a higher yield. Some crop varieties display a certain degree of tolerance, which has been exploited by plant breeders to develop varieties that thrive under stress conditions. Moreover, a long list of genes involved in abiotic stress tolerance have been identified and characterized by molecular techniques and overexpressed individually in plant transformation experiments. Nevertheless, stress tolerance phenotypes are polygenetic traits, which current genomic tools are dissecting to exploit their use by accelerating genetic introgression using molecular markers or site-directed mutagenesis such as CRISPR-Cas9. In this review, we describe plant mechanisms to sense and tolerate adverse climate conditions and examine and discuss classic and new molecular tools to select and improve abiotic stress tolerance in major crops.
Collapse
Affiliation(s)
- Miguel Angel Villalobos-López
- Laboratorio de Genómica Funcional y Biotecnología de Plantas, Centro de Investigación en Biotecnología Aplicada, Instituto Politécnico Nacional, Ex-Hacienda San Juan Molino Carretera Estatal Km 1.5, Santa Inés-Tecuexcomac-Tepetitla 90700, Tlaxcala, Mexico
| | - Analilia Arroyo-Becerra
- Laboratorio de Genómica Funcional y Biotecnología de Plantas, Centro de Investigación en Biotecnología Aplicada, Instituto Politécnico Nacional, Ex-Hacienda San Juan Molino Carretera Estatal Km 1.5, Santa Inés-Tecuexcomac-Tepetitla 90700, Tlaxcala, Mexico
| | - Anareli Quintero-Jiménez
- División de Estudios de Posgrado e Investigación, Tecnológico Nacional de México/I.T. Roque, Km. 8 Carretera Celaya-Juventino Rosas, Roque, Celaya 38110, Guanajato, Mexico
| | - Gabriel Iturriaga
- División de Estudios de Posgrado e Investigación, Tecnológico Nacional de México/I.T. Roque, Km. 8 Carretera Celaya-Juventino Rosas, Roque, Celaya 38110, Guanajato, Mexico
| |
Collapse
|
16
|
Yadav B, Kaur V, Narayan OP, Yadav SK, Kumar A, Wankhede DP. Integrated omics approaches for flax improvement under abiotic and biotic stress: Current status and future prospects. FRONTIERS IN PLANT SCIENCE 2022; 13:931275. [PMID: 35958216 PMCID: PMC9358615 DOI: 10.3389/fpls.2022.931275] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 06/27/2022] [Indexed: 05/03/2023]
Abstract
Flax (Linum usitatissimum L.) or linseed is one of the important industrial crops grown all over the world for seed oil and fiber. Besides oil and fiber, flax offers a wide range of nutritional and therapeutic applications as a feed and food source owing to high amount of α-linolenic acid (omega-3 fatty acid), lignans, protein, minerals, and vitamins. Periodic losses caused by unpredictable environmental stresses such as drought, heat, salinity-alkalinity, and diseases pose a threat to meet the rising market demand. Furthermore, these abiotic and biotic stressors have a negative impact on biological diversity and quality of oil/fiber. Therefore, understanding the interaction of genetic and environmental factors in stress tolerance mechanism and identification of underlying genes for economically important traits is critical for flax improvement and sustainability. In recent technological era, numerous omics techniques such as genomics, transcriptomics, metabolomics, proteomics, phenomics, and ionomics have evolved. The advancements in sequencing technologies accelerated development of genomic resources which facilitated finer genetic mapping, quantitative trait loci (QTL) mapping, genome-wide association studies (GWAS), and genomic selection in major cereal and oilseed crops including flax. Extensive studies in the area of genomics and transcriptomics have been conducted post flax genome sequencing. Interestingly, research has been focused more for abiotic stresses tolerance compared to disease resistance in flax through transcriptomics, while the other areas of omics such as metabolomics, proteomics, ionomics, and phenomics are in the initial stages in flax and several key questions remain unanswered. Little has been explored in the integration of omic-scale data to explain complex genetic, physiological and biochemical basis of stress tolerance in flax. In this review, the current status of various omics approaches for elucidation of molecular pathways underlying abiotic and biotic stress tolerance in flax have been presented and the importance of integrated omics technologies in future research and breeding have been emphasized to ensure sustainable yield in challenging environments.
Collapse
Affiliation(s)
- Bindu Yadav
- Division of Germplasm Evaluation, ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
| | - Vikender Kaur
- Division of Germplasm Evaluation, ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
| | - Om Prakash Narayan
- College of Arts and Sciences, University of Florida, Gainesville, FL, United States
| | - Shashank Kumar Yadav
- Division of Germplasm Evaluation, ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
| | - Ashok Kumar
- Division of Germplasm Evaluation, ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
| | | |
Collapse
|