1
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Bhandari P, Lee TG. Using machine learning and partial dependence to evaluate robustness of best linear unbiased prediction (BLUP) for phenotypic values. J Appl Genet 2024; 65:283-286. [PMID: 38170439 DOI: 10.1007/s13353-023-00815-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 11/17/2023] [Accepted: 11/30/2023] [Indexed: 01/05/2024]
Abstract
Best linear unbiased prediction (BLUP) is widely used in plant research to address experimental variation. For phenotypic values, BLUP accuracy is largely dependent on properly controlled experimental repetition and how variable components are outlined in the model. Thus, determining BLUP robustness implies the need to evaluate contributions from each repetition. Here, we assessed the robustness of BLUP values for simulated or empirical phenotypic datasets, where the BLUP value and each experimental repetition served as dependent and independent (feature) variables, respectively. Our technique incorporated machine learning and partial dependence. First, we compared the feature importance estimated with the neural networks. Second, we compared estimated average marginal effects of individual repetitions, calculated with a partial dependence analysis. We showed that contributions of experimental repetitions are unequal in a phenotypic dataset, suggesting that the calculated BLUP value is likely to be influenced by some repetitions more than others (such as failing to detect simulated true positive associations). To resolve disproportionate sources, variable components in the BLUP model must be further outlined.
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Affiliation(s)
- Prashant Bhandari
- Horticultural Sciences Department, University of Florida, Gainesville, FL, 32611, USA
| | - Tong Geon Lee
- Horticultural Sciences Department, University of Florida, Gainesville, FL, 32611, USA.
- Bayer, Chesterfield, MO, 63017, USA.
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2
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Deng Z, Zhou P, Hu W, Wang X, Gong R. Biomimetic multilayer film simulating solar spectrum reflection characteristics of natural vegetations for optical camouflage. OPTICS EXPRESS 2023; 31:37082-37093. [PMID: 38017845 DOI: 10.1364/oe.501565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 10/09/2023] [Indexed: 11/30/2023]
Abstract
The camouflage for developed hyperspectral detection technology, which can accurately distinguish the spectrum between object and background, has emerged as an important unsolved challenge. In this study, a biomimetic film (Ge/ZnS multilayer structure) for optical camouflage of hyperspectral and laser with color simulation has been proposed and experimentally demonstrated. By taking advantage of the wavelength selective property of Ge/ZnS multilayer through film interference, the biomimetic film which can simulate the reflection spectral characteristics of vegetation background and eliminate laser signal has been realized based on inverse design. The selective narrowband absorption can manipulate the contrary condition for hyperspectral camouflage (high reflectance in 0.8-1.3 µm) and laser camouflage (low reflectance at 1.06 µm) in the same waveband. The planarized biomimetic multilayer film presents several distinct advantages: (1) elaborate simulation of vegetation reflectance spectrum for hyperspectral camouflage (the spectral similarity coefficient of 92.1%), and efficient absorption at 1.06 µm for laser camouflage (reflectance of 17.8%); (2) tunable color chrominance of various vegetation types for visual camouflage; (3) thermally robust camouflage performance (up to 250 °C) due to temperature endurable property of Ge and ZnS. The hyperspectral-laser camouflage film expands the design strategy of optical camouflage application.
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3
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Herr AW, Carter AH. Remote sensing continuity: a comparison of HTP platforms and potential challenges with field applications. FRONTIERS IN PLANT SCIENCE 2023; 14:1233892. [PMID: 37790786 PMCID: PMC10544974 DOI: 10.3389/fpls.2023.1233892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 08/29/2023] [Indexed: 10/05/2023]
Abstract
In an era of climate change and increased environmental variability, breeders are looking for tools to maintain and increase genetic gain and overall efficiency. In recent years the field of high throughput phenotyping (HTP) has received increased attention as an option to meet this need. There are many platform options in HTP, but ground-based handheld and remote aerial systems are two popular options. While many HTP setups have similar specifications, it is not always clear if data from different systems can be treated interchangeably. In this research, we evaluated two handheld radiometer platforms, Cropscan MSR16R and Spectra Vista Corp (SVC) HR-1024i, as well as a UAS-based system with a Sentera Quad Multispectral Sensor. Each handheld radiometer was used for two years simultaneously with the unoccupied aircraft systems (UAS) in collecting winter wheat breeding trials between 2018-2021. Spectral reflectance indices (SRI) were calculated for each system. SRI heritability and correlation were analyzed in evaluating the platform and SRI usability for breeding applications. Correlations of SRIs were low against UAS SRI and grain yield while using the Cropscan system in 2018 and 2019. Dissimilarly, the SVC system in 2020 and 2021 produced moderate correlations across UAS SRI and grain yield. UAS SRI were consistently more heritable, with broad-sense heritability ranging from 0.58 to 0.80. Data standardization and collection windows are important to consider in ensuring reliable data. Furthermore, practical aspects and best practices for these HTP platforms, relative to applied breeding applications, are highlighted and discussed. The findings of this study can be a framework to build upon when considering the implementation of HTP technology in an applied breeding program.
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Affiliation(s)
| | - Arron H. Carter
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
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4
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Gonzalez EM, Zarei A, Hendler N, Simmons T, Zarei A, Demieville J, Strand R, Rozzi B, Calleja S, Ellingson H, Cosi M, Davey S, Lavelle DO, Truco MJ, Swetnam TL, Merchant N, Michelmore RW, Lyons E, Pauli D. PhytoOracle: Scalable, modular phenomics data processing pipelines. FRONTIERS IN PLANT SCIENCE 2023; 14:1112973. [PMID: 36950362 PMCID: PMC10025408 DOI: 10.3389/fpls.2023.1112973] [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: 11/30/2022] [Accepted: 02/14/2023] [Indexed: 06/18/2023]
Abstract
As phenomics data volume and dimensionality increase due to advancements in sensor technology, there is an urgent need to develop and implement scalable data processing pipelines. Current phenomics data processing pipelines lack modularity, extensibility, and processing distribution across sensor modalities and phenotyping platforms. To address these challenges, we developed PhytoOracle (PO), a suite of modular, scalable pipelines for processing large volumes of field phenomics RGB, thermal, PSII chlorophyll fluorescence 2D images, and 3D point clouds. PhytoOracle aims to (i) improve data processing efficiency; (ii) provide an extensible, reproducible computing framework; and (iii) enable data fusion of multi-modal phenomics data. PhytoOracle integrates open-source distributed computing frameworks for parallel processing on high-performance computing, cloud, and local computing environments. Each pipeline component is available as a standalone container, providing transferability, extensibility, and reproducibility. The PO pipeline extracts and associates individual plant traits across sensor modalities and collection time points, representing a unique multi-system approach to addressing the genotype-phenotype gap. To date, PO supports lettuce and sorghum phenotypic trait extraction, with a goal of widening the range of supported species in the future. At the maximum number of cores tested in this study (1,024 cores), PO processing times were: 235 minutes for 9,270 RGB images (140.7 GB), 235 minutes for 9,270 thermal images (5.4 GB), and 13 minutes for 39,678 PSII images (86.2 GB). These processing times represent end-to-end processing, from raw data to fully processed numerical phenotypic trait data. Repeatability values of 0.39-0.95 (bounding area), 0.81-0.95 (axis-aligned bounding volume), 0.79-0.94 (oriented bounding volume), 0.83-0.95 (plant height), and 0.81-0.95 (number of points) were observed in Field Scanalyzer data. We also show the ability of PO to process drone data with a repeatability of 0.55-0.95 (bounding area).
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Affiliation(s)
| | - Ariyan Zarei
- Department of Computer Science, University of Arizona, Tucson, AZ, United States
| | - Nathanial Hendler
- School of Plant Sciences, University of Arizona, Tucson, AZ, United States
| | - Travis Simmons
- School of Plant Sciences, University of Arizona, Tucson, AZ, United States
| | - Arman Zarei
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
| | - Jeffrey Demieville
- School of Plant Sciences, University of Arizona, Tucson, AZ, United States
| | - Robert Strand
- School of Plant Sciences, University of Arizona, Tucson, AZ, United States
| | - Bruno Rozzi
- School of Plant Sciences, University of Arizona, Tucson, AZ, United States
| | - Sebastian Calleja
- School of Plant Sciences, University of Arizona, Tucson, AZ, United States
| | - Holly Ellingson
- Data Science Institute, University of Arizona, Tucson, AZ, United States
| | - Michele Cosi
- School of Plant Sciences, University of Arizona, Tucson, AZ, United States
- BIO5 Institute, University of Arizona, Tucson, AZ, United States
| | - Sean Davey
- Department of Cellular and Molecular Medicine, University of Arizona, Tucson, AZ, United States
| | - Dean O. Lavelle
- The Genome and Biomedical Sciences Facility, University of California, Davis, Davis, CA, United States
| | - Maria José Truco
- The Genome and Biomedical Sciences Facility, University of California, Davis, Davis, CA, United States
| | - Tyson L. Swetnam
- BIO5 Institute, University of Arizona, Tucson, AZ, United States
- School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, United States
| | - Nirav Merchant
- Data Science Institute, University of Arizona, Tucson, AZ, United States
- BIO5 Institute, University of Arizona, Tucson, AZ, United States
| | - Richard W. Michelmore
- The Genome and Biomedical Sciences Facility, University of California, Davis, Davis, CA, United States
- Department of Plant Sciences, University of California, Davis, Davis, CA, United States
| | - Eric Lyons
- School of Plant Sciences, University of Arizona, Tucson, AZ, United States
- Data Science Institute, University of Arizona, Tucson, AZ, United States
- BIO5 Institute, University of Arizona, Tucson, AZ, United States
| | - Duke Pauli
- School of Plant Sciences, University of Arizona, Tucson, AZ, United States
- Data Science Institute, University of Arizona, Tucson, AZ, United States
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5
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Zheng C, Abd-Elrahman A, Whitaker VM, Dalid C. Deep Learning for Strawberry Canopy Delineation and Biomass Prediction from High-Resolution Images. PLANT PHENOMICS (WASHINGTON, D.C.) 2022; 2022:9850486. [PMID: 36320455 PMCID: PMC9595049 DOI: 10.34133/2022/9850486] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 09/12/2022] [Indexed: 05/30/2023]
Abstract
Modeling plant canopy biophysical parameters at the individual plant level remains a major challenge. This study presents a workflow for automatic strawberry canopy delineation and biomass prediction from high-resolution images using deep neural networks. High-resolution (5 mm) RGB orthoimages, near-infrared (NIR) orthoimages, and Digital Surface Models (DSM), which were generated by Structure from Motion (SfM), were utilized in this study. Mask R-CNN was applied to the orthoimages of two band combinations (RGB and RGB-NIR) to identify and delineate strawberry plant canopies. The average detection precision rate and recall rate were 97.28% and 99.71% for RGB images and 99.13% and 99.54% for RGB-NIR images, and the mean intersection over union (mIoU) rates for instance segmentation were 98.32% and 98.45% for RGB and RGB-NIR images, respectively. Based on the center of the canopy mask, we imported the cropped RGB, NIR, DSM, and mask images of individual plants to vanilla deep regression models to model canopy leaf area and dry biomass. Two networks (VGG-16 and ResNet-50) were used as the backbone architecture for feature map extraction. The R 2 values of dry biomass models were about 0.76 and 0.79 for the VGG-16 and ResNet-50 networks, respectively. Similarly, the R 2 values of leaf area were 0.82 and 0.84, respectively. The RMSE values were approximately 8.31 and 8.73 g for dry biomass analyzed using the VGG-16 and ResNet-50 networks, respectively. Leaf area RMSE was 0.05 m2 for both networks. This work demonstrates the feasibility of deep learning networks in individual strawberry plant extraction and biomass estimation.
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Affiliation(s)
- Caiwang Zheng
- Gulf Coast Research and Education Center, University of Florida, Wimauma, FL 33598, USA
- School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL 32603, USA
| | - Amr Abd-Elrahman
- Gulf Coast Research and Education Center, University of Florida, Wimauma, FL 33598, USA
- School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL 32603, USA
| | - Vance M. Whitaker
- Gulf Coast Research and Education Center, University of Florida, Wimauma, FL 33598, USA
- Horticultural Sciences Department, University of Florida, Gainesville, FL 32603, USA
| | - Cheryl Dalid
- Gulf Coast Research and Education Center, University of Florida, Wimauma, FL 33598, USA
- Horticultural Sciences Department, University of Florida, Gainesville, FL 32603, USA
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6
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Chen Q, Zheng B, Chenu K, Hu P, Chapman SC. Unsupervised Plot-Scale LAI Phenotyping via UAV-Based Imaging, Modelling, and Machine Learning. PLANT PHENOMICS 2022; 2022:9768253. [PMID: 35935677 PMCID: PMC9317541 DOI: 10.34133/2022/9768253] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 05/25/2022] [Indexed: 11/14/2022]
Abstract
High-throughput phenotyping has become the frontier to accelerate breeding through linking genetics to crop growth estimation, which requires accurate estimation of leaf area index (LAI). This study developed a hybrid method to train the random forest regression (RFR) models with synthetic datasets generated by a radiative transfer model to estimate LAI from UAV-based multispectral images. The RFR models were evaluated on both (i) subsets from the synthetic datasets and (ii) observed data from two field experiments (i.e., Exp16, Exp19). Given the parameter ranges and soil reflectance are well calibrated in synthetic training data, RFR models can accurately predict LAI from canopy reflectance captured in field conditions, with systematic overestimation for LAI<2 due to background effect, which can be addressed by applying background correction on original reflectance map based on vegetation-background classification. Overall, RFR models achieved accurate LAI prediction from background-corrected reflectance for Exp16 (correlation coefficient (r) of 0.95, determination coefficient (R2) of 0.90~0.91, root mean squared error (RMSE) of 0.36~0.40 m2 m−2, relative root mean squared error (RRMSE) of 25~28%) and less accurate for Exp19 (r =0.80~0.83, R2 = 0.63~0.69, RMSE of 0.84~0.86 m2 m−2, RRMSE of 30~31%). Additionally, RFR models correctly captured spatiotemporal variation of observed LAI as well as identified variations for different growing stages and treatments in terms of genotypes and management practices (i.e., planting density, irrigation, and fertilization) for two experiments. The developed hybrid method allows rapid, accurate, nondestructive phenotyping of the dynamics of LAI during vegetative growth to facilitate assessments of growth rate including in breeding program assessments.
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Affiliation(s)
- Qiaomin Chen
- School of Agriculture and Food Sciences, The University of Queensland, St Lucia, QLD, Australia
- Agriculture and Food, CSIRO, Queensland Bioscience Precinct, St Lucia, QLD, Australia
| | - Bangyou Zheng
- Agriculture and Food, CSIRO, Queensland Bioscience Precinct, St Lucia, QLD, Australia
| | - Karine Chenu
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Toowoomba, QLD, Australia
| | - Pengcheng Hu
- School of Agriculture and Food Sciences, The University of Queensland, St Lucia, QLD, Australia
- Agriculture and Food, CSIRO, Queensland Bioscience Precinct, St Lucia, QLD, Australia
| | - Scott C. Chapman
- School of Agriculture and Food Sciences, The University of Queensland, St Lucia, QLD, Australia
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7
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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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8
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Bartholomé J, Prakash PT, Cobb JN. Genomic Prediction: Progress and Perspectives for Rice Improvement. Methods Mol Biol 2022; 2467:569-617. [PMID: 35451791 DOI: 10.1007/978-1-0716-2205-6_21] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Genomic prediction can be a powerful tool to achieve greater rates of genetic gain for quantitative traits if thoroughly integrated into a breeding strategy. In rice as in other crops, the interest in genomic prediction is very strong with a number of studies addressing multiple aspects of its use, ranging from the more conceptual to the more practical. In this chapter, we review the literature on rice (Oryza sativa) and summarize important considerations for the integration of genomic prediction in breeding programs. The irrigated breeding program at the International Rice Research Institute is used as a concrete example on which we provide data and R scripts to reproduce the analysis but also to highlight practical challenges regarding the use of predictions. The adage "To someone with a hammer, everything looks like a nail" describes a common psychological pitfall that sometimes plagues the integration and application of new technologies to a discipline. We have designed this chapter to help rice breeders avoid that pitfall and appreciate the benefits and limitations of applying genomic prediction, as it is not always the best approach nor the first step to increasing the rate of genetic gain in every context.
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Affiliation(s)
- Jérôme Bartholomé
- CIRAD, UMR AGAP Institut, Montpellier, France.
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Montpellier SupAgro, Montpellier, France.
- Rice Breeding Platform, International Rice Research Institute, Manila, Philippines.
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9
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Hamazaki K, Iwata H. Bayesian optimization of multivariate genomic prediction models based on secondary traits for improved accuracy gains and phenotyping costs. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:35-50. [PMID: 34609531 DOI: 10.1007/s00122-021-03949-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Accepted: 09/14/2021] [Indexed: 06/13/2023]
Abstract
We propose a novel approach to the Bayesian optimization of multivariate genomic prediction models based on secondary traits to improve accuracy gains and phenotyping costs via efficient Pareto frontier estimation. Multivariate genomic prediction based on secondary traits, such as data from various omics technologies including high-throughput phenotyping (e.g., unmanned aerial vehicle-based remote sensing), has attracted much attention because it offers improved accuracy gains compared with genomic prediction based only on marker genotypes. Although there is a trade-off between accuracy gains and phenotyping costs of secondary traits, no attempt has been made to optimize these trade-offs. In this study, we propose a novel approach to optimize multivariate genomic prediction models for secondary traits measurable at early growth stages for improved accuracy gains and phenotyping costs. The proposed approach employs Bayesian optimization for efficient Pareto frontier estimation, representing the maximum accuracy at a given cost. The proposed approach successfully estimated the optimal secondary trait combinations across a range of costs while providing genomic predictions for only about [Formula: see text] of all possible combinations. The simulation results reflecting the characteristics of each scenario of the simulated target traits showed that the obtained optimal combinations were reasonable. Analysis of real-time target trait data showed that the proposed multivariate genomic prediction model had significantly superior accuracy compared to the univariate genomic prediction model.
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Affiliation(s)
- Kosuke Hamazaki
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan
- JSPS Research Fellow, Tokyo, Japan
| | - Hiroyoshi Iwata
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan.
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10
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Rogers AR, Holland JB. Environment-specific genomic prediction ability in maize using environmental covariates depends on environmental similarity to training data. G3 (BETHESDA, MD.) 2021; 12:6486423. [PMID: 35100364 PMCID: PMC9245610 DOI: 10.1093/g3journal/jkab440] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 12/06/2021] [Indexed: 12/30/2022]
Abstract
Technology advances have made possible the collection of a wealth of genomic, environmental, and phenotypic data for use in plant breeding. Incorporation of environmental data into environment-specific genomic prediction is hindered in part because of inherently high data dimensionality. Computationally efficient approaches to combining genomic and environmental information may facilitate extension of genomic prediction models to new environments and germplasm, and better understanding of genotype-by-environment (G × E) interactions. Using genomic, yield trial, and environmental data on 1,918 unique hybrids evaluated in 59 environments from the maize Genomes to Fields project, we determined that a set of 10,153 SNP dominance coefficients and a 5-day temporal window size for summarizing environmental variables were optimal for genomic prediction using only genetic and environmental main effects. Adding marker-by-environment variable interactions required dimension reduction, and we found that reducing dimensionality of the genetic data while keeping the full set of environmental covariates was best for environment-specific genomic prediction of grain yield, leading to an increase in prediction ability of 2.7% to achieve a prediction ability of 80% across environments when data were masked at random. We then measured how prediction ability within environments was affected under stratified training-testing sets to approximate scenarios commonly encountered by plant breeders, finding that incorporation of marker-by-environment effects improved prediction ability in cases where training and test sets shared environments, but did not improve prediction in new untested environments. The environmental similarity between training and testing sets had a greater impact on the efficacy of prediction than genetic similarity between training and test sets.
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Affiliation(s)
- Anna R Rogers
- Program in Genetics, North Carolina State University, Raleigh, NC
27695, USA
| | - James B Holland
- Program in Genetics, North Carolina State University, Raleigh, NC
27695, USA,USDA-ARS Plant Science Research Unit, North Carolina State
University, Raleigh, NC 27695, USA,Department of Crop and Soil Sciences, North Carolina State
University, Raleigh, NC 27695, USA,Corresponding author: Department of Agriculture—Agriculture
Research Service, Box 7620 North Carolina State University, Raleigh, NC 27695-7620, USA.
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11
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Bacher H, Zhu F, Gao T, Liu K, Dhatt BK, Awada T, Zhang C, Distelfeld A, Yu H, Peleg Z, Walia H. Wild emmer introgression alters root-to-shoot growth dynamics in durum wheat in response to water stress. PLANT PHYSIOLOGY 2021; 187:1149-1162. [PMID: 34618034 PMCID: PMC8566259 DOI: 10.1093/plphys/kiab292] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 06/07/2021] [Indexed: 06/13/2023]
Abstract
Water deficit during the early vegetative growth stages of wheat (Triticum) can limit shoot growth and ultimately impact grain productivity. Introducing diversity in wheat cultivars to enhance the range of phenotypic responses to water limitations during vegetative growth can provide potential avenues for mitigating subsequent yield losses. We tested this hypothesis in an elite durum wheat background by introducing a series of introgressions from a wild emmer (Triticum turgidum ssp. dicoccoides) wheat. Wild emmer populations harbor rich phenotypic diversity for drought-adaptive traits. To determine the effect of these introgressions on vegetative growth under water-limited conditions, we used image-based phenotyping to catalog divergent growth responses to water stress ranging from high plasticity to high stability. One of the introgression lines exhibited a significant shift in root-to-shoot ratio in response to water stress. We characterized this shift by combining genetic analysis and root transcriptome profiling to identify candidate genes (including a root-specific kinase) that may be linked to the root-to-shoot carbon reallocation under water stress. Our results highlight the potential of introducing functional diversity into elite durum wheat for enhancing the range of water stress adaptation.
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Affiliation(s)
- Harel Bacher
- The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, Rehovot, Israel
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Feiyu Zhu
- Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Tian Gao
- Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Kan Liu
- School of Biological Sciences, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Balpreet K Dhatt
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Tala Awada
- School of Natural Resources, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Chi Zhang
- School of Biological Sciences, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | | | - Hongfeng Yu
- Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Zvi Peleg
- The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Harkamal Walia
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
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12
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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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13
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Zhao Y, Zheng B, Chapman SC, Laws K, George-Jaeggli B, Hammer GL, Jordan DR, Potgieter AB. Detecting Sorghum Plant and Head Features from Multispectral UAV Imagery. PLANT PHENOMICS (WASHINGTON, D.C.) 2021; 2021:9874650. [PMID: 34676373 PMCID: PMC8502246 DOI: 10.34133/2021/9874650] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 08/31/2021] [Indexed: 06/03/2023]
Abstract
In plant breeding, unmanned aerial vehicles (UAVs) carrying multispectral cameras have demonstrated increasing utility for high-throughput phenotyping (HTP) to aid the interpretation of genotype and environment effects on morphological, biochemical, and physiological traits. A key constraint remains the reduced resolution and quality extracted from "stitched" mosaics generated from UAV missions across large areas. This can be addressed by generating high-quality reflectance data from a single nadir image per plot. In this study, a pipeline was developed to derive reflectance data from raw multispectral UAV images that preserve the original high spatial and spectral resolutions and to use these for phenotyping applications. Sequential steps involved (i) imagery calibration, (ii) spectral band alignment, (iii) backward calculation, (iv) plot segmentation, and (v) application. Each step was designed and optimised to estimate the number of plants and count sorghum heads within each breeding plot. Using a derived nadir image of each plot, the coefficients of determination were 0.90 and 0.86 for estimates of the number of sorghum plants and heads, respectively. Furthermore, the reflectance information acquired from the different spectral bands showed appreciably high discriminative ability for sorghum head colours (i.e., red and white). Deployment of this pipeline allowed accurate segmentation of crop organs at the canopy level across many diverse field plots with minimal training needed from machine learning approaches.
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Affiliation(s)
- Yan Zhao
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, Gatton, Queensland 4343, Australia
| | - Bangyou Zheng
- CSIRO Agriculture and Food, St. Lucia, Queensland 4072, Australia
| | - Scott C. Chapman
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, Gatton, Queensland 4343, Australia
- The University of Queensland, School of Agriculture and Food Sciences, St. Lucia, Queensland 4072, Australia
| | - Kenneth Laws
- Department of Agriculture and Fisheries, Agri-Science Queensland, Warwick, Queensland 4370, Australia
| | - Barbara George-Jaeggli
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, Gatton, Queensland 4343, Australia
- Department of Agriculture and Fisheries, Agri-Science Queensland, Warwick, Queensland 4370, Australia
| | - Graeme L. Hammer
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, Gatton, Queensland 4343, Australia
| | - David R. Jordan
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, Gatton, Queensland 4343, Australia
- Department of Agriculture and Fisheries, Agri-Science Queensland, Warwick, Queensland 4370, Australia
| | - Andries B. Potgieter
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Centre for Crop Science, Gatton, Queensland 4343, Australia
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14
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Hu P, Chapman SC, Zheng B. Coupling of machine learning methods to improve estimation of ground coverage from unmanned aerial vehicle (UAV) imagery for high-throughput phenotyping of crops. FUNCTIONAL PLANT BIOLOGY : FPB 2021; 48:766-779. [PMID: 33663681 DOI: 10.1071/fp20309] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 02/14/2021] [Indexed: 06/12/2023]
Abstract
Ground coverage (GC) allows monitoring of crop growth and development and is normally estimated as the ratio of vegetation to the total pixels from nadir images captured by visible-spectrum (RGB) cameras. The accuracy of estimated GC can be significantly impacted by the effect of 'mixed pixels', which is related to the spatial resolution of the imagery as determined by flight altitude, camera resolution and crop characteristics (fine vs coarse textures). In this study, a two-step machine learning method was developed to improve the accuracy of GC of wheat (Triticum aestivum L.) estimated from coarse-resolution RGB images captured by an unmanned aerial vehicle (UAV) at higher altitudes. The classification tree-based per-pixel segmentation (PPS) method was first used to segment fine-resolution reference images into vegetation and background pixels. The reference and their segmented images were degraded to the target coarse spatial resolution. These degraded images were then used to generate a training dataset for a regression tree-based model to establish the sub-pixel classification (SPC) method. The newly proposed method (i.e. PPS-SPC) was evaluated with six synthetic and four real UAV image sets (SISs and RISs, respectively) with different spatial resolutions. Overall, the results demonstrated that the PPS-SPC method obtained higher accuracy of GC in both SISs and RISs comparing to PPS method, with root mean squared errors (RMSE) of less than 6% and relative RMSE (RRMSE) of less than 11% for SISs, and RMSE of less than 5% and RRMSE of less than 35% for RISs. The proposed PPS-SPC method can be potentially applied in plant breeding and precision agriculture to balance accuracy requirement and UAV flight height in the limited battery life and operation time.
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Affiliation(s)
- Pengcheng Hu
- CSIRO Agriculture and Food, Queensland Biosciences Precinct 306 Carmody Road, St Lucia 4067, Qld, Australia
| | - Scott C Chapman
- CSIRO Agriculture and Food, Queensland Biosciences Precinct 306 Carmody Road, St Lucia 4067, Qld, Australia; and School of Food and Agricultural Sciences, The University of Queensland, via Warrego Highway, Gatton 4343, Qld, Australia
| | - Bangyou Zheng
- CSIRO Agriculture and Food, Queensland Biosciences Precinct 306 Carmody Road, St Lucia 4067, Qld, Australia; and Corresponding author.
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15
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Smith DT, Potgieter AB, Chapman SC. Scaling up high-throughput phenotyping for abiotic stress selection in the field. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1845-1866. [PMID: 34076731 DOI: 10.1007/s00122-021-03864-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 05/13/2021] [Indexed: 05/18/2023]
Abstract
High-throughput phenotyping (HTP) is in its infancy for deployment in large-scale breeding programmes. With the ability to measure correlated traits associated with physiological ideotypes, in-field phenotyping methods are available for screening of abiotic stress responses. As cropping environments become more hostile and unpredictable due to the effects of climate change, the need to characterise variability across spatial and temporal scales will become increasingly important. The sensor technologies that have enabled HTP from macroscopic through to satellite sensors may also be utilised here to complement spatial characterisation using envirotyping, which can improve estimations of genotypic performance across environments by better accounting for variation at the plot, trial and inter-trial levels. Climate change is leading to increased variation at all physical and temporal scales in the cropping environment. Maintaining yield stability under circumstances with greater levels of abiotic stress while capitalising upon yield potential in good years, requires approaches to plant breeding that target the physiological limitations to crop performance in specific environments. This requires dynamic modelling of conditions within target populations of environments, GxExM predictions, clustering of environments so breeding trajectories can be defined, and the development of screens that enable selection for genetic gain to occur. High-throughput phenotyping (HTP), combined with related technologies used for envirotyping, can help to address these challenges. Non-destructive analysis of the morphological, biochemical and physiological qualities of plant canopies using HTP has great potential to complement whole-genome selection, which is becoming increasingly common in breeding programmes. A range of novel analytic techniques, such as machine learning and deep learning, combined with a widening range of sensors, allow rapid assessment of large breeding populations that are repeatable and objective. Secondary traits underlying radiation use efficiency and water use efficiency can be screened with HTP for selection at the early stages of a breeding programme. HTP and envirotyping technologies can also characterise spatial variability at trial and within-plot levels, which can be used to correct for spatial variations that confound measurements of genotypic values. This review explores HTP for abiotic stress selection through a physiological trait lens and additionally investigates the use of envirotyping and EC to characterise spatial variability at all physical scales in METs.
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Affiliation(s)
- Daniel T Smith
- The University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia
| | - Andries B Potgieter
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD, 4072, Australia
| | - Scott C Chapman
- The University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia.
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16
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Dowd T, McInturf S, Li M, Topp CN. Rated-M for mesocosm: allowing the multimodal analysis of mature root systems in 3D. Emerg Top Life Sci 2021; 5:249-260. [PMID: 33555320 PMCID: PMC8166344 DOI: 10.1042/etls20200278] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 01/12/2021] [Accepted: 01/18/2021] [Indexed: 11/17/2022]
Abstract
A plants' water and nutrients are primarily absorbed through roots, which in a natural setting is highly dependent on the 3-dimensional configuration of the root system, collectively known as root system architecture (RSA). RSA is difficult to study due to a variety of factors, accordingly, an arsenal of methods have been developed to address the challenges of both growing root systems for imaging, and the imaging methods themselves, although there is no 'best' method as each has its own spectrum of trade-offs. Here, we describe several methods for plant growth or imaging. Then, we introduce the adaptation and integration of three complementary methods, root mesocosms, photogrammetry, and electrical resistance tomography (ERT). Mesocosms can allow for unconstrained root growth, excavation and preservation of 3-dimensional RSA, and modularity that facilitates the use of a variety of sensors. The recovered root system can be digitally reconstructed through photogrammetry, which is an inexpensive method requiring only an appropriate studio space and a digital camera. Lastly, we demonstrate how 3-dimensional water availability can be measured using ERT inside of root mesocosms.
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Affiliation(s)
- Tyler Dowd
- Topp Lab, Donald Danforth Plant Science Center, 975 N Warson Road, St. Louis, MO, 63124 U.S.A
| | - Samuel McInturf
- Topp Lab, Donald Danforth Plant Science Center, 975 N Warson Road, St. Louis, MO, 63124 U.S.A
| | - Mao Li
- Topp Lab, Donald Danforth Plant Science Center, 975 N Warson Road, St. Louis, MO, 63124 U.S.A
| | - Christopher N Topp
- Topp Lab, Donald Danforth Plant Science Center, 975 N Warson Road, St. Louis, MO, 63124 U.S.A
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17
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Lv C, Zu M, Xie D, Cheng H. Emulating Solar Spectral Reflectance of Natural Leaf with Bionic Leaf Prepared from 4A Zeolite-Derived Ultramarine Green Pigment. MATERIALS 2021; 14:ma14061406. [PMID: 33799377 PMCID: PMC8001328 DOI: 10.3390/ma14061406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Revised: 03/07/2021] [Accepted: 03/08/2021] [Indexed: 11/16/2022]
Abstract
The emulation of the reflectance of green leaf in the solar spectral band (300–2500 nm) has garnered increasing attention from researchers. Currently, various materials have been proposed and investigated as potential bionic leaves. However, the problems such as poor weather durability, heavy metal pollution, and complex preparation technology still persist. Herein, a bionic leaf is prepared from an ultramarine green pigment as the functional material, polyvinylidene fluoride (PVDF) as the film-forming material, and LiCl as the humidizer. To prepare the ultramarine green pigment, the sulfur anion is added into the β cage of the 4A zeolite. The mechanisms and properties were discussed based on X-ray diffraction (XRD), scanning electron microscopy (SEM), Raman spectroscopy, and spectroscopic methods. The results show that the as-fabricated bionic leaf based on the 4A zeolite-derived ultramarine green pigment was able to demonstrate a high spectral similarity coefficient of 0.91 with the green leaf. Furthermore, the spectral similarity coefficient was increased to 0.94 after being subjected to a simulated rainforest environment for 48 h, which indicated its high weather durability.
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Affiliation(s)
| | - Mei Zu
- Correspondence: ; Tel./Fax: +86-731-8457-6440
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18
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Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming. REMOTE SENSING 2021. [DOI: 10.3390/rs13030531] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Measurement of plant characteristics is still the primary bottleneck in both plant breeding and crop management. Rapid and accurate acquisition of information about large plant populations is critical for monitoring plant health and dissecting the underlying genetic traits. In recent years, high-throughput phenotyping technology has benefitted immensely from both remote sensing and machine learning. Simultaneous use of multiple sensors (e.g., high-resolution RGB, multispectral, hyperspectral, chlorophyll fluorescence, and light detection and ranging (LiDAR)) allows a range of spatial and spectral resolutions depending on the trait in question. Meanwhile, computer vision and machine learning methodology have emerged as powerful tools for extracting useful biological information from image data. Together, these tools allow the evaluation of various morphological, structural, biophysical, and biochemical traits. In this review, we focus on the recent development of phenomics approaches in strawberry farming, particularly those utilizing remote sensing and machine learning, with an eye toward future prospects for strawberries in precision agriculture. The research discussed is broadly categorized according to strawberry traits related to (1) fruit/flower detection, fruit maturity, fruit quality, internal fruit attributes, fruit shape, and yield prediction; (2) leaf and canopy attributes; (3) water stress; and (4) pest and disease detection. Finally, we present a synthesis of the potential research opportunities and directions that could further promote the use of remote sensing and machine learning in strawberry farming.
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19
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Cooper M, Messina CD. Can We Harness "Enviromics" to Accelerate Crop Improvement by Integrating Breeding and Agronomy? FRONTIERS IN PLANT SCIENCE 2021; 12:735143. [PMID: 34567047 PMCID: PMC8461239 DOI: 10.3389/fpls.2021.735143] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 08/16/2021] [Indexed: 05/02/2023]
Abstract
The diverse consequences of genotype-by-environment (GxE) interactions determine trait phenotypes across levels of biological organization for crops, challenging our ambition to predict trait phenotypes from genomic information alone. GxE interactions have many implications for optimizing both genetic gain through plant breeding and crop productivity through on-farm agronomic management. Advances in genomics technologies have provided many suitable predictors for the genotype dimension of GxE interactions. Emerging advances in high-throughput proximal and remote sensor technologies have stimulated the development of "enviromics" as a community of practice, which has the potential to provide suitable predictors for the environment dimension of GxE interactions. Recently, several bespoke examples have emerged demonstrating the nascent potential for enhancing the prediction of yield and other complex trait phenotypes of crop plants through including effects of GxE interactions within prediction models. These encouraging results motivate the development of new prediction methods to accelerate crop improvement. If we can automate methods to identify and harness suitable sets of coordinated genotypic and environmental predictors, this will open new opportunities to upscale and operationalize prediction of the consequences of GxE interactions. This would provide a foundation for accelerating crop improvement through integrating the contributions of both breeding and agronomy. Here we draw on our experience from improvement of maize productivity for the range of water-driven environments across the US corn-belt. We provide perspectives from the maize case study to prioritize promising opportunities to further develop and automate "enviromics" methodologies to accelerate crop improvement through integrated breeding and agronomic approaches for a wider range of crops and environmental targets.
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Affiliation(s)
- Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD, Australia
- *Correspondence: Mark Cooper,
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20
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Shao MR, Jiang N, Li M, Howard A, Lehner K, Mullen JL, Gunn SL, McKay JK, Topp CN. Complementary Phenotyping of Maize Root System Architecture by Root Pulling Force and X-Ray Imaging. PLANT PHENOMICS (WASHINGTON, D.C.) 2021; 2021:9859254. [PMID: 34870229 PMCID: PMC8603028 DOI: 10.34133/2021/9859254] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 10/05/2021] [Indexed: 05/11/2023]
Abstract
The root system is critical for the survival of nearly all land plants and a key target for improving abiotic stress tolerance, nutrient accumulation, and yield in crop species. Although many methods of root phenotyping exist, within field studies, one of the most popular methods is the extraction and measurement of the upper portion of the root system, known as the root crown, followed by trait quantification based on manual measurements or 2D imaging. However, 2D techniques are inherently limited by the information available from single points of view. Here, we used X-ray computed tomography to generate highly accurate 3D models of maize root crowns and created computational pipelines capable of measuring 71 features from each sample. This approach improves estimates of the genetic contribution to root system architecture and is refined enough to detect various changes in global root system architecture over developmental time as well as more subtle changes in root distributions as a result of environmental differences. We demonstrate that root pulling force, a high-throughput method of root extraction that provides an estimate of root mass, is associated with multiple 3D traits from our pipeline. Our combined methodology can therefore be used to calibrate and interpret root pulling force measurements across a range of experimental contexts or scaled up as a stand-alone approach in large genetic studies of root system architecture.
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Affiliation(s)
- M. R. Shao
- Donald Danforth Plant Science Center, Saint Louis, MO, USA
| | - N. Jiang
- Donald Danforth Plant Science Center, Saint Louis, MO, USA
| | - M. Li
- Donald Danforth Plant Science Center, Saint Louis, MO, USA
| | - A. Howard
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO, USA
| | - K. Lehner
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO, USA
| | - J. L. Mullen
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO, USA
| | - S. L. Gunn
- Donald Danforth Plant Science Center, Saint Louis, MO, USA
| | - J. K. McKay
- Department of Agricultural Biology, Colorado State University, Fort Collins, CO, USA
| | - C. N. Topp
- Donald Danforth Plant Science Center, Saint Louis, MO, USA
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21
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UAV-Based Multispectral Phenotyping for Disease Resistance to Accelerate Crop Improvement under Changing Climate Conditions. REMOTE SENSING 2020. [DOI: 10.3390/rs12152445] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Accelerating crop improvement for increased yield and better adaptation to changing climatic conditions is an issue of increasing urgency in order to satisfy the ever-increasing global food demand. However, the major bottleneck is the absence of high-throughput plant phenotyping methods for rapid and cost-effective data-driven variety selection and release in plant breeding. Traditional phenotyping methods that rely on trained experts are slow, costly, labor-intensive, subjective, and often require destructive sampling. We explore ways to improve the efficiency of crop phenotyping through the use of unmanned aerial vehicle (UAV)-based multispectral remotely sensed data in maize (Zea mays L.) varietal response to maize streak virus (MSV) disease. Twenty-five maize varieties grown in a trial with three replications were evaluated under artificial MSV inoculation. Ground scoring for MSV infection was carried out at mid-vegetative, flowering, and mid-grain filling on a scale of 1 (resistant) to 9 (susceptible). UAV-derived spectral data were acquired at these three different phenological stages in multispectral bands corresponding to Green (0.53–0.57 μm), Red (0.64–0.68 μm), Rededge (0.73–0.74 μm), and Near-Infrared (0.77–0.81 μm). The imagery captured was stitched together in Pix4Dmapper, which generates two types of multispectral orthomosaics: the NoAlpha and the transparent mosaics for each band. The NoAlpha imagery was used as input into QGIS to extract reflectance data. Six vegetation indices were derived for each variety: normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), Rededge NDVI (NDVIrededge), Simple Ratio (SR), green Chlorophyll Index (CIgreen), and Rededge Chlorophyll Index (CIrededge). The Random Forest (RF) classifier was used to evaluate UAV-derived spectral and VIs with and without variable optimization. Correlations between the UAV-derived data and manual MSV scores were significant (R = 0.74–0.84). Varieties were classified into resistant, moderately resistant, and susceptible with overall classification accuracies of 77.3% (Kappa = 0.64) with optimized and 68.2% (Kappa = 0.51) without optimized variables, representing an improvement of ~13.3% due to variable optimization. The RF model selected GNDVI, CIgreen, CIrededge, and the Red band as the most important variables for classification. Mid-vegetative was the most ideal phenological stage for accurate varietal phenotyping and discrimination using UAV-derived multispectral data with RF under artificial MSV inoculation. The results provide a rapid UAV-based remote sensing solution that offers a step-change towards data availability at high spatial (submeter) and temporal (daily/weekly) resolution in varietal analysis for quick and robust high-throughput plant phenotyping, important for timely and unbiased data-driven variety selection and release in plant breeding programs, especially as climate change accelerates.
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22
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Weckwerth W, Ghatak A, Bellaire A, Chaturvedi P, Varshney RK. PANOMICS meets germplasm. PLANT BIOTECHNOLOGY JOURNAL 2020; 18:1507-1525. [PMID: 32163658 PMCID: PMC7292548 DOI: 10.1111/pbi.13372] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Revised: 02/17/2020] [Accepted: 02/26/2020] [Indexed: 05/14/2023]
Abstract
Genotyping-by-sequencing has enabled approaches for genomic selection to improve yield, stress resistance and nutritional value. More and more resource studies are emerging providing 1000 and more genotypes and millions of SNPs for one species covering a hitherto inaccessible intraspecific genetic variation. The larger the databases are growing, the better statistical approaches for genomic selection will be available. However, there are clear limitations on the statistical but also on the biological part. Intraspecific genetic variation is able to explain a high proportion of the phenotypes, but a large part of phenotypic plasticity also stems from environmentally driven transcriptional, post-transcriptional, translational, post-translational, epigenetic and metabolic regulation. Moreover, regulation of the same gene can have different phenotypic outputs in different environments. Consequently, to explain and understand environment-dependent phenotypic plasticity based on the available genotype variation we have to integrate the analysis of further molecular levels reflecting the complete information flow from the gene to metabolism to phenotype. Interestingly, metabolomics platforms are already more cost-effective than NGS platforms and are decisive for the prediction of nutritional value or stress resistance. Here, we propose three fundamental pillars for future breeding strategies in the framework of Green Systems Biology: (i) combining genome selection with environment-dependent PANOMICS analysis and deep learning to improve prediction accuracy for marker-dependent trait performance; (ii) PANOMICS resolution at subtissue, cellular and subcellular level provides information about fundamental functions of selected markers; (iii) combining PANOMICS with genome editing and speed breeding tools to accelerate and enhance large-scale functional validation of trait-specific precision breeding.
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Affiliation(s)
- Wolfram Weckwerth
- Molecular Systems Biology (MOSYS)Department of Functional and Evolutionary EcologyFaculty of Life SciencesUniversity of ViennaViennaAustria
- Vienna Metabolomics Center (VIME)University of ViennaViennaAustria
| | - Arindam Ghatak
- Molecular Systems Biology (MOSYS)Department of Functional and Evolutionary EcologyFaculty of Life SciencesUniversity of ViennaViennaAustria
| | - Anke Bellaire
- Molecular Systems Biology (MOSYS)Department of Functional and Evolutionary EcologyFaculty of Life SciencesUniversity of ViennaViennaAustria
| | - Palak Chaturvedi
- Molecular Systems Biology (MOSYS)Department of Functional and Evolutionary EcologyFaculty of Life SciencesUniversity of ViennaViennaAustria
| | - Rajeev K. Varshney
- Center of Excellence in Genomics & Systems BiologyInternational Crops Research Institute for the Semi‐Arid Tropics (ICRISAT)HyderabadTelanganaIndia
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23
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Weckwerth W, Ghatak A, Bellaire A, Chaturvedi P, Varshney RK. PANOMICS meets germplasm. PLANT BIOTECHNOLOGY JOURNAL 2020; 18. [PMID: 32163658 PMCID: PMC7292548 DOI: 10.1111/pbi.13372,10.13140/rg.2.1.1233.5760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Genotyping-by-sequencing has enabled approaches for genomic selection to improve yield, stress resistance and nutritional value. More and more resource studies are emerging providing 1000 and more genotypes and millions of SNPs for one species covering a hitherto inaccessible intraspecific genetic variation. The larger the databases are growing, the better statistical approaches for genomic selection will be available. However, there are clear limitations on the statistical but also on the biological part. Intraspecific genetic variation is able to explain a high proportion of the phenotypes, but a large part of phenotypic plasticity also stems from environmentally driven transcriptional, post-transcriptional, translational, post-translational, epigenetic and metabolic regulation. Moreover, regulation of the same gene can have different phenotypic outputs in different environments. Consequently, to explain and understand environment-dependent phenotypic plasticity based on the available genotype variation we have to integrate the analysis of further molecular levels reflecting the complete information flow from the gene to metabolism to phenotype. Interestingly, metabolomics platforms are already more cost-effective than NGS platforms and are decisive for the prediction of nutritional value or stress resistance. Here, we propose three fundamental pillars for future breeding strategies in the framework of Green Systems Biology: (i) combining genome selection with environment-dependent PANOMICS analysis and deep learning to improve prediction accuracy for marker-dependent trait performance; (ii) PANOMICS resolution at subtissue, cellular and subcellular level provides information about fundamental functions of selected markers; (iii) combining PANOMICS with genome editing and speed breeding tools to accelerate and enhance large-scale functional validation of trait-specific precision breeding.
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Affiliation(s)
- Wolfram Weckwerth
- Molecular Systems Biology (MOSYS)Department of Functional and Evolutionary EcologyFaculty of Life SciencesUniversity of ViennaViennaAustria
- Vienna Metabolomics Center (VIME)University of ViennaViennaAustria
| | - Arindam Ghatak
- Molecular Systems Biology (MOSYS)Department of Functional and Evolutionary EcologyFaculty of Life SciencesUniversity of ViennaViennaAustria
| | - Anke Bellaire
- Molecular Systems Biology (MOSYS)Department of Functional and Evolutionary EcologyFaculty of Life SciencesUniversity of ViennaViennaAustria
| | - Palak Chaturvedi
- Molecular Systems Biology (MOSYS)Department of Functional and Evolutionary EcologyFaculty of Life SciencesUniversity of ViennaViennaAustria
| | - Rajeev K. Varshney
- Center of Excellence in Genomics & Systems BiologyInternational Crops Research Institute for the Semi‐Arid Tropics (ICRISAT)HyderabadTelanganaIndia
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Moreira FF, Oliveira HR, Volenec JJ, Rainey KM, Brito LF. Integrating High-Throughput Phenotyping and Statistical Genomic Methods to Genetically Improve Longitudinal Traits in Crops. FRONTIERS IN PLANT SCIENCE 2020; 11:681. [PMID: 32528513 PMCID: PMC7264266 DOI: 10.3389/fpls.2020.00681] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 04/30/2020] [Indexed: 05/28/2023]
Abstract
The rapid development of remote sensing in agronomic research allows the dynamic nature of longitudinal traits to be adequately described, which may enhance the genetic improvement of crop efficiency. For traits such as light interception, biomass accumulation, and responses to stressors, the data generated by the various high-throughput phenotyping (HTP) methods requires adequate statistical techniques to evaluate phenotypic records throughout time. As a consequence, information about plant functioning and activation of genes, as well as the interaction of gene networks at different stages of plant development and in response to environmental stimulus can be exploited. In this review, we outline the current analytical approaches in quantitative genetics that are applied to longitudinal traits in crops throughout development, describe the advantages and pitfalls of each approach, and indicate future research directions and opportunities.
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Affiliation(s)
- Fabiana F. Moreira
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Hinayah R. Oliveira
- Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
| | - Jeffrey J. Volenec
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Katy M. Rainey
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Luiz F. Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
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25
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Anderson SL, Murray SC, Chen Y, Malambo L, Chang A, Popescu S, Cope D, Jung J. Unoccupied aerial system enabled functional modeling of maize height reveals dynamic expression of loci. PLANT DIRECT 2020; 4:e00223. [PMID: 32399510 PMCID: PMC7212003 DOI: 10.1002/pld3.223] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 03/25/2020] [Accepted: 04/15/2020] [Indexed: 05/06/2023]
Abstract
Unoccupied aerial systems (UAS) were used to phenotype growth trajectories of inbred maize populations under field conditions. Three recombinant inbred line populations were surveyed on a weekly basis collecting RGB images across two irrigation regimens (irrigated and non-irrigated/rain fed). Plant height, estimated by the 95th percentile (P95) height from UAS generated 3D point clouds, exceeded 70% correlation (r) to manual ground truth measurements and 51% of experimental variance was explained by genetics. The Weibull sigmoidal function accurately modeled plant growth (R 2: >99%; RMSE: <4 cm) from P95 genetic means. The mean asymptote was strongly correlated (r 2 = 0.66-0.77) with terminal plant height. Maximum absolute growth rates (mm/day) were weakly correlated with height and flowering time. The average inflection point ranged from 57 to 60 days after sowing (DAS) and was correlated with flowering time (r 2 = 0.45-0.68). Functional growth parameters (asymptote, inflection point, growth rate) alone identified 34 genetic loci, each explaining 3-15% of total genetic variation. Plant height was estimated at one-day intervals to 85 DAS, identifying 58 unique temporal quantitative trait loci (QTL) locations. Genomic hotspots on chromosomes 1 and 3 indicated chromosomal regions associated with functional growth trajectories influencing flowering time, growth rate, and terminal growth. Temporal QTL demonstrated unique dynamic expression patterns not previously observable, and no QTL were significantly expressed throughout the entire growing season. UAS technologies improved phenotypic selection accuracy and permitted monitoring traits on a temporal scale previously infeasible using manual measurements, furthering understanding of crop development and biological trajectories.
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Affiliation(s)
- Steven L. Anderson
- Department of Soil and Crop SciencesTexas A&M UniversityCollege StationTXUSA
- Present address:
Department of Environmental HorticultureInstitute of Food and Agricultural SciencesMid‐Florida Research and Education CenterUniversity of FloridaApopkaFLUSA
| | - Seth C. Murray
- Department of Soil and Crop SciencesTexas A&M UniversityCollege StationTXUSA
| | - Yuanyuan Chen
- Department of Soil and Crop SciencesTexas A&M UniversityCollege StationTXUSA
- Present address:
National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanChina
| | - Lonesome Malambo
- Department of Ecosystem Science and ManagementTexas A&M UniversityCollege StationTXUSA
| | - Anjin Chang
- School of Engineering and Computer SciencesTexas A&M University – Corpus ChristiCorpus ChristiTXUSA
| | - Sorin Popescu
- Department of Ecosystem Science and ManagementTexas A&M UniversityCollege StationTXUSA
| | - Dale Cope
- Department of Mechanical EngineeringTexas A&M UniversityCollege StationTXUSA
| | - Jinha Jung
- School of Engineering and Computer SciencesTexas A&M University – Corpus ChristiCorpus ChristiTXUSA
- Present address:
Department of Civil EngineeringPurdue UniversityWest LafayetteINUSA
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26
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Honecker A, Schumann H, Becirevic D, Klingbeil L, Volland K, Forberig S, Jansen M, Paulsen H, Kuhlmann H, Léon J. Plant, space and time - linked together in an integrative and scalable data management system for phenomic approaches in agronomic field trials. PLANT METHODS 2020; 16:55. [PMID: 32336978 PMCID: PMC7171732 DOI: 10.1186/s13007-020-00596-3] [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: 11/21/2019] [Accepted: 04/04/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND To ensure further genetic gain, genomic approaches in plant breeding rely on precise phenotypic data, describing plant structure, function and performance. A more precise characterization of the environment will allow a better dealing with genotype-by-environment-by-management interactions. Therefore, space and time dependencies of the crop production processes have to be considered. The use of novel sensor technologies has drastically increased the amount and diversity of phenotypic data from agronomic field trials. Existing data management systems either do not consider space and time, are not customizable to individual needs such as field trial handling, or have restricted availability. Hence, we propose an integrative data management and information system (DMIS) for handling of traditional and novel sensor-based phenotypic, environmental and management data. The DMIS must be customizable, applicable and scalable from individual users to organizations. RESULTS Key element of the system is a dynamic PostgreSQL database with GIS-extension, capable of importing, storing and managing all types of data including images. The database references every structural database object and measurement in a threefold approach with semantic, spatial and temporal reference. Timestamps and geo-coordinates allow automated linking of all data. Traits can be precisely defined individually or uploaded as predefined lists. Filtering and selection routines allow compilation of all data for visualization via tables, charts or maps and for export and external statistical analysis. New possibilities of environmental information-based planning of field trials, weather-guided phenotyping and data analysis for outlier or hot-spot detection are demonstrated. CONCLUSIONS The DMIS supports users in handling experimental field trials with crop plants and modern phenotyping methods. It focuses on linking all space and time dependent processes of plant production. Weather, soil and management, as well as growth and yield formation of the plants can be depicted, thus allowing a more precise interpretation of the results in relation to environment and management. Breeders, extension specialists, official testing agencies and agricultural scientists are assisted in all steps of a typical workflow with planning, designing, conducting, controlling and analyzing field trials to generate new information for decision support in the crop improvement process.
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Affiliation(s)
- Andreas Honecker
- INRES-Plant Breeding, University of Bonn, Katzenburgweg 5, 53115 Bonn, Germany
| | - Henrik Schumann
- INRES-Plant Breeding, University of Bonn, Katzenburgweg 5, 53115 Bonn, Germany
| | - Diana Becirevic
- IGG-Geodesy, University of Bonn, Nussallee 17, 53115 Bonn, Germany
| | - Lasse Klingbeil
- IGG-Geodesy, University of Bonn, Nussallee 17, 53115 Bonn, Germany
| | - Kai Volland
- Terrestris GmbH & Co. KG, Kölnstraße 99, 53111 Bonn, Germany
| | - Steffi Forberig
- Terrestris GmbH & Co. KG, Kölnstraße 99, 53111 Bonn, Germany
| | - Marc Jansen
- Terrestris GmbH & Co. KG, Kölnstraße 99, 53111 Bonn, Germany
| | - Hinrich Paulsen
- Terrestris GmbH & Co. KG, Kölnstraße 99, 53111 Bonn, Germany
| | - Heiner Kuhlmann
- IGG-Geodesy, University of Bonn, Nussallee 17, 53115 Bonn, Germany
| | - Jens Léon
- INRES-Plant Breeding, University of Bonn, Katzenburgweg 5, 53115 Bonn, Germany
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27
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Lyra DH, Virlet N, Sadeghi-Tehran P, Hassall KL, Wingen LU, Orford S, Griffiths S, Hawkesford MJ, Slavov GT. Functional QTL mapping and genomic prediction of canopy height in wheat measured using a robotic field phenotyping platform. JOURNAL OF EXPERIMENTAL BOTANY 2020; 71:1885-1898. [PMID: 32097472 PMCID: PMC7094083 DOI: 10.1093/jxb/erz545] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Accepted: 02/19/2020] [Indexed: 05/08/2023]
Abstract
Genetic studies increasingly rely on high-throughput phenotyping, but the resulting longitudinal data pose analytical challenges. We used canopy height data from an automated field phenotyping platform to compare several approaches to scanning for quantitative trait loci (QTLs) and performing genomic prediction in a wheat recombinant inbred line mapping population based on up to 26 sampled time points (TPs). We detected four persistent QTLs (i.e. expressed for most of the growing season), with both empirical and simulation analyses demonstrating superior statistical power of detecting such QTLs through functional mapping approaches compared with conventional individual TP analyses. In contrast, even very simple individual TP approaches (e.g. interval mapping) had superior detection power for transient QTLs (i.e. expressed during very short periods). Using spline-smoothed phenotypic data resulted in improved genomic predictive abilities (5-8% higher than individual TP prediction), while the effect of including significant QTLs in prediction models was relatively minor (<1-4% improvement). Finally, although QTL detection power and predictive ability generally increased with the number of TPs analysed, gains beyond five or 10 TPs chosen based on phenological information had little practical significance. These results will inform the development of an integrated, semi-automated analytical pipeline, which will be more broadly applicable to similar data sets in wheat and other crops.
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Affiliation(s)
- Danilo H Lyra
- Department of Computational & Analytical Sciences, Rothamsted Research, Harpenden, UK
| | - Nicolas Virlet
- Department of Plant Sciences, Rothamsted Research, Harpenden, UK
| | | | - Kirsty L Hassall
- Department of Computational & Analytical Sciences, Rothamsted Research, Harpenden, UK
| | - Luzie U Wingen
- John Innes Centre, Norwich Research Park, Colney Lane, Norwich, UK
| | - Simon Orford
- John Innes Centre, Norwich Research Park, Colney Lane, Norwich, UK
| | - Simon Griffiths
- John Innes Centre, Norwich Research Park, Colney Lane, Norwich, UK
| | | | - Gancho T Slavov
- Department of Computational & Analytical Sciences, Rothamsted Research, Harpenden, UK
- Scion, Rotorua, New Zealand
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28
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Lyra DH, Virlet N, Sadeghi-Tehran P, Hassall KL, Wingen LU, Orford S, Griffiths S, Hawkesford MJ, Slavov GT. Functional QTL mapping and genomic prediction of canopy height in wheat measured using a robotic field phenotyping platform. JOURNAL OF EXPERIMENTAL BOTANY 2020. [PMID: 32097472 DOI: 10.17632/pkxpkw6j43.2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Genetic studies increasingly rely on high-throughput phenotyping, but the resulting longitudinal data pose analytical challenges. We used canopy height data from an automated field phenotyping platform to compare several approaches to scanning for quantitative trait loci (QTLs) and performing genomic prediction in a wheat recombinant inbred line mapping population based on up to 26 sampled time points (TPs). We detected four persistent QTLs (i.e. expressed for most of the growing season), with both empirical and simulation analyses demonstrating superior statistical power of detecting such QTLs through functional mapping approaches compared with conventional individual TP analyses. In contrast, even very simple individual TP approaches (e.g. interval mapping) had superior detection power for transient QTLs (i.e. expressed during very short periods). Using spline-smoothed phenotypic data resulted in improved genomic predictive abilities (5-8% higher than individual TP prediction), while the effect of including significant QTLs in prediction models was relatively minor (<1-4% improvement). Finally, although QTL detection power and predictive ability generally increased with the number of TPs analysed, gains beyond five or 10 TPs chosen based on phenological information had little practical significance. These results will inform the development of an integrated, semi-automated analytical pipeline, which will be more broadly applicable to similar data sets in wheat and other crops.
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Affiliation(s)
- Danilo H Lyra
- Department of Computational & Analytical Sciences, Rothamsted Research, Harpenden, UK
| | - Nicolas Virlet
- Department of Plant Sciences, Rothamsted Research, Harpenden, UK
| | | | - Kirsty L Hassall
- Department of Computational & Analytical Sciences, Rothamsted Research, Harpenden, UK
| | - Luzie U Wingen
- John Innes Centre, Norwich Research Park, Colney Lane, Norwich, UK
| | - Simon Orford
- John Innes Centre, Norwich Research Park, Colney Lane, Norwich, UK
| | - Simon Griffiths
- John Innes Centre, Norwich Research Park, Colney Lane, Norwich, UK
| | | | - Gancho T Slavov
- Department of Computational & Analytical Sciences, Rothamsted Research, Harpenden, UK
- Scion, Rotorua, New Zealand
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29
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Fernández-Calleja M, Monteagudo A, Casas AM, Boutin C, Pin PA, Morales F, Igartua E. Rapid On-Site Phenotyping via Field Fluorimeter Detects Differences in Photosynthetic Performance in a Hybrid-Parent Barley Germplasm Set. SENSORS (BASEL, SWITZERLAND) 2020; 20:E1486. [PMID: 32182722 PMCID: PMC7085516 DOI: 10.3390/s20051486] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 03/03/2020] [Accepted: 03/05/2020] [Indexed: 12/15/2022]
Abstract
Crop productivity can be expressed as the product of the amount of radiation intercepted, radiation use efficiency and harvest index. Genetic variation for components of radiation use efficiency has rarely been explored due to the lack of appropriate equipment to determine parameters at the scale needed in plant breeding. On the other hand, responses of the photosynthetic apparatus to environmental conditions have not been extensively investigated under field conditions, due to the challenges posed by the fluctuating environmental conditions. This study applies a rapid, low-cost, and reliable high-throughput phenotyping tool to explore genotypic variation for photosynthetic performance of a set of hybrid barleys and their parents under mild water-stress and unstressed field conditions. We found differences among the genotypic sets that are relevant for plant breeders and geneticists. Hybrids showed lower leaf temperature differential and higher non-photochemical quenching, resembling closer the male parents. The combination of traits detected in hybrids seems favorable, and could indicate improved photoprotection and better fitness under stress conditions. Additionally, we proved the potential of a low-cost, field-based phenotyping equipment to be used routinely in barley breeding programs for early screening for stress tolerance.
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Affiliation(s)
- Miriam Fernández-Calleja
- Department of Genetics and Plant Production, Aula Dei Experimental Station (EEAD-CSIC), Avda. Montañana 1005, E-50059 Zaragoza, Spain; (M.F.-C.); (A.M.); (A.M.C.)
| | - Arantxa Monteagudo
- Department of Genetics and Plant Production, Aula Dei Experimental Station (EEAD-CSIC), Avda. Montañana 1005, E-50059 Zaragoza, Spain; (M.F.-C.); (A.M.); (A.M.C.)
| | - Ana M. Casas
- Department of Genetics and Plant Production, Aula Dei Experimental Station (EEAD-CSIC), Avda. Montañana 1005, E-50059 Zaragoza, Spain; (M.F.-C.); (A.M.); (A.M.C.)
| | - Christophe Boutin
- Syngenta Seeds SAS, 12 Chemin de l’Hobit, 31790 Saint Sauveur, France; (C.B.); (P.A.P.)
| | - Pierre A. Pin
- Syngenta Seeds SAS, 12 Chemin de l’Hobit, 31790 Saint Sauveur, France; (C.B.); (P.A.P.)
| | - Fermín Morales
- Agrobiotechnology Institute (IdAB), CSIC-Gobierno de Navarra, Avda. de Pamplona 123, 31192 Mutilva, Navarra, Spain;
| | - Ernesto Igartua
- Department of Genetics and Plant Production, Aula Dei Experimental Station (EEAD-CSIC), Avda. Montañana 1005, E-50059 Zaragoza, Spain; (M.F.-C.); (A.M.); (A.M.C.)
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30
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McFarland BA, AlKhalifah N, Bohn M, Bubert J, Buckler ES, Ciampitti I, Edwards J, Ertl D, Gage JL, Falcon CM, Flint-Garcia S, Gore MA, Graham C, Hirsch CN, Holland JB, Hood E, Hooker D, Jarquin D, Kaeppler SM, Knoll J, Kruger G, Lauter N, Lee EC, Lima DC, Lorenz A, Lynch JP, McKay J, Miller ND, Moose SP, Murray SC, Nelson R, Poudyal C, Rocheford T, Rodriguez O, Romay MC, Schnable JC, Schnable PS, Scully B, Sekhon R, Silverstein K, Singh M, Smith M, Spalding EP, Springer N, Thelen K, Thomison P, Tuinstra M, Wallace J, Walls R, Wills D, Wisser RJ, Xu W, Yeh CT, de Leon N. Maize genomes to fields (G2F): 2014-2017 field seasons: genotype, phenotype, climatic, soil, and inbred ear image datasets. BMC Res Notes 2020; 13:71. [PMID: 32051026 PMCID: PMC7017475 DOI: 10.1186/s13104-020-4922-8] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Accepted: 01/27/2020] [Indexed: 11/10/2022] Open
Abstract
OBJECTIVES Advanced tools and resources are needed to efficiently and sustainably produce food for an increasing world population in the context of variable environmental conditions. The maize genomes to fields (G2F) initiative is a multi-institutional initiative effort that seeks to approach this challenge by developing a flexible and distributed infrastructure addressing emerging problems. G2F has generated large-scale phenotypic, genotypic, and environmental datasets using publicly available inbred lines and hybrids evaluated through a network of collaborators that are part of the G2F's genotype-by-environment (G × E) project. This report covers the public release of datasets for 2014-2017. DATA DESCRIPTION Datasets include inbred genotypic information; phenotypic, climatic, and soil measurements and metadata information for each testing location across years. For a subset of inbreds in 2014 and 2015, yield component phenotypes were quantified by image analysis. Data released are accompanied by README descriptions. For genotypic and phenotypic data, both raw data and a version without outliers are reported. For climatic data, a version calibrated to the nearest airport weather station and a version without outliers are reported. The 2014 and 2015 datasets are updated versions from the previously released files [1] while 2016 and 2017 datasets are newly available to the public.
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Affiliation(s)
| | | | - Martin Bohn
- University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Jessica Bubert
- University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Edward S Buckler
- Cornell University, Ithaca, NY, 14853, USA.,USDA-ARS, Beltsville, MD, USA
| | | | - Jode Edwards
- USDA-ARS, Beltsville, MD, USA.,Iowa State University, Ames, IA, 50011, USA
| | - David Ertl
- Iowa Corn Growers Association, Johnston, IA, 50131, USA
| | | | | | - Sherry Flint-Garcia
- USDA-ARS, Beltsville, MD, USA.,University of Missouri, Columbia, MO, 65211, USA
| | | | | | | | - James B Holland
- USDA-ARS, Beltsville, MD, USA.,North Carolina State University, Raleigh, NC, 27695, USA
| | | | | | | | | | | | - Greg Kruger
- University of Nebraska, Lincoln, NE, 68583, USA
| | - Nick Lauter
- USDA-ARS, Beltsville, MD, USA.,Iowa State University, Ames, IA, 50011, USA
| | | | | | - Aaron Lorenz
- University of Minnesota, St. Paul, MN, 55108, USA
| | | | - John McKay
- Colorado State University, Fort Collins, CO, 80523, USA
| | | | - Stephen P Moose
- University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Seth C Murray
- Texas A&M University, College Station, TX, 77843, USA
| | | | | | | | | | | | | | | | - Brian Scully
- USDA-ARS, Beltsville, MD, USA.,University of Florida, Gainesville, FL, 32611, USA
| | | | | | | | | | | | | | - Kurt Thelen
- Michigan State University, East Lansing, MI, 48824, USA
| | | | | | | | | | - David Wills
- University of Missouri, Columbia, MO, 65211, USA
| | | | - Wenwei Xu
- Texas A&M University, College Station, TX, 77843, USA
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31
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Dos Santos JPR, Fernandes SB, McCoy S, Lozano R, Brown PJ, Leakey ADB, Buckler ES, Garcia AAF, Gore MA. Novel Bayesian Networks for Genomic Prediction of Developmental Traits in Biomass Sorghum. G3 (BETHESDA, MD.) 2020; 10:769-781. [PMID: 31852730 PMCID: PMC7003104 DOI: 10.1534/g3.119.400759] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2019] [Accepted: 12/15/2019] [Indexed: 11/23/2022]
Abstract
The ability to connect genetic information between traits over time allow Bayesian networks to offer a powerful probabilistic framework to construct genomic prediction models. In this study, we phenotyped a diversity panel of 869 biomass sorghum (Sorghum bicolor (L.) Moench) lines, which had been genotyped with 100,435 SNP markers, for plant height (PH) with biweekly measurements from 30 to 120 days after planting (DAP) and for end-of-season dry biomass yield (DBY) in four environments. We evaluated five genomic prediction models: Bayesian network (BN), Pleiotropic Bayesian network (PBN), Dynamic Bayesian network (DBN), multi-trait GBLUP (MTr-GBLUP), and multi-time GBLUP (MTi-GBLUP) models. In fivefold cross-validation, prediction accuracies ranged from 0.46 (PBN) to 0.49 (MTr-GBLUP) for DBY and from 0.47 (DBN, DAP120) to 0.75 (MTi-GBLUP, DAP60) for PH. Forward-chaining cross-validation further improved prediction accuracies of the DBN, MTi-GBLUP and MTr-GBLUP models for PH (training slice: 30-45 DAP) by 36.4-52.4% relative to the BN and PBN models. Coincidence indices (target: biomass, secondary: PH) and a coincidence index based on lines (PH time series) showed that the ranking of lines by PH changed minimally after 45 DAP. These results suggest a two-level indirect selection method for PH at harvest (first-level target trait) and DBY (second-level target trait) could be conducted earlier in the season based on ranking of lines by PH at 45 DAP (secondary trait). With the advance of high-throughput phenotyping technologies, our proposed two-level indirect selection framework could be valuable for enhancing genetic gain per unit of time when selecting on developmental traits.
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Affiliation(s)
- Jhonathan P R Dos Santos
- Plant Breeding and Genetics Section, School of Integrative Plant Science
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, SP, Brazil
| | | | | | - Roberto Lozano
- Plant Breeding and Genetics Section, School of Integrative Plant Science
| | - Patrick J Brown
- Section of Agricultural Plant Biology, Department of Plant Sciences, University of California Davis, 95616, and
| | - Andrew D B Leakey
- Department of Crop Science
- Institute for Genomic Biology
- Department of Plant Biology, University of Illinois at Urbana Champaign, 61801
| | - Edward S Buckler
- Plant Breeding and Genetics Section, School of Integrative Plant Science
- United States Department of Agriculture, Agricultural Research Service, R. W. Holley Center, Ithaca, New York 14853
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853
| | - Antonio A F Garcia
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, SP, Brazil,
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science,
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32
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Marzougui A, Ma Y, McGee RJ, Khot LR, Sankaran S. Generalized Linear Model with Elastic Net Regularization and Convolutional Neural Network for Evaluating Aphanomyces Root Rot Severity in Lentil. PLANT PHENOMICS (WASHINGTON, D.C.) 2020; 2020:2393062. [PMID: 33575665 PMCID: PMC7870103 DOI: 10.34133/2020/2393062] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Accepted: 09/21/2020] [Indexed: 05/21/2023]
Abstract
Phenomics technologies allow quantitative assessment of phenotypes across a larger number of plant genotypes compared to traditional phenotyping approaches. The utilization of such technologies has enabled the generation of multidimensional plant traits creating big datasets. However, to harness the power of phenomics technologies, more sophisticated data analysis methods are required. In this study, Aphanomyces root rot (ARR) resistance in 547 lentil accessions and lines was evaluated using Red-Green-Blue (RGB) images of roots. We created a dataset of 6,460 root images that were annotated by a plant breeder based on the disease severity. Two approaches, generalized linear model with elastic net regularization (EN) and convolutional neural network (CNN), were developed to classify disease resistance categories into three classes: resistant, partially resistant, and susceptible. The results indicated that the selected image features using EN models were able to classify three disease categories with an accuracy of up to 0.91 ± 0.004 (0.96 ± 0.005 resistant, 0.82 ± 0.009 partially resistant, and 0.92 ± 0.007 susceptible) compared to CNN with an accuracy of about 0.84 ± 0.009 (0.96 ± 0.008 resistant, 0.68 ± 0.026 partially resistant, and 0.83 ± 0.015 susceptible). The resistant class was accurately detected using both classification methods. However, partially resistant class was challenging to detect as the features (data) of the partially resistant class often overlapped with those of resistant and susceptible classes. Collectively, the findings provided insights on the use of phenomics techniques and machine learning approaches to provide quantitative measures of ARR resistance in lentil.
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Affiliation(s)
- Afef Marzougui
- Department of Biological Systems Engineering, Washington State University, Pullman, WA, USA
| | - Yu Ma
- Department of Horticulture, Washington State University, Pullman, WA, USA
| | - Rebecca J. McGee
- United States Department of Agriculture-Agricultural Research Service, Grain Legume Genetics and Physiology Research Unit, Washington State University, Pullman, WA, USA
| | - Lav R. Khot
- Department of Biological Systems Engineering, Washington State University, Pullman, WA, USA
| | - Sindhuja Sankaran
- Department of Biological Systems Engineering, Washington State University, Pullman, WA, USA
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33
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Rose T, Kage H. The Contribution of Functional Traits to the Breeding Progress of Central-European Winter Wheat Under Differing Crop Management Intensities. FRONTIERS IN PLANT SCIENCE 2019; 10:1521. [PMID: 31867026 PMCID: PMC6908521 DOI: 10.3389/fpls.2019.01521] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Accepted: 10/31/2019] [Indexed: 05/15/2023]
Abstract
Wheat yields in many of the main producing European countries stagnate since about 20 years. Hence, it is of high interest, to analyze breeding progress in terms of yield and how associated traits changed. Therefore, a set of 42 cultivars (released between 1966 and 2012) was selected and yield as well as functional traits defined by the Monteith and Moss equation were evaluated under three levels of management intensity. The Monteith Moss equation thereby calculates grain yield as the product of incident photosynthetically active radiation, fraction of intercepted radiation, radiation use efficiency, and harvest index. The field trial was performed in a high yielding environment in Northern Germany in two seasons (2016-2017 and 2017-2018) with very contrasting rainfall rates. The three differing managements were: intensive (high N + pesticides), semi-intensive (high N - pesticides), and extensive (low N - pesticides). The results indicate that the stagnation of wheat yields in Central-Europe is not caused by a diminishing effect of breeding on yield potential. This equally applies to suboptimal growing conditions like extensified crop management and restricted water supply. Nearly all functional sub-traits showed a parallel progress but coefficients of determination of relationships between traits and year of variety release are decreasing along the hierarchy of yield formation. One exception is radiation interception which did not show a stable linear increase during breeding history. In recent years, biomass is getting more important in comparison to harvest index. Values of harvest index are slowly approaching theoretical maxima and correlations with grain yield are decreasing.
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Affiliation(s)
- Till Rose
- Institute of Crop Science and Plant Breeding, Agronomy and Crop Science, Christian-Albrechts-University, Kiel, Germany
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Bustos-Korts D, Boer MP, Malosetti M, Chapman S, Chenu K, Zheng B, van Eeuwijk FA. Combining Crop Growth Modeling and Statistical Genetic Modeling to Evaluate Phenotyping Strategies. FRONTIERS IN PLANT SCIENCE 2019; 10:1491. [PMID: 31827479 PMCID: PMC6890853 DOI: 10.3389/fpls.2019.01491] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 10/28/2019] [Indexed: 05/25/2023]
Abstract
Genomic prediction of complex traits, say yield, benefits from including information on correlated component traits. Statistical criteria to decide which yield components to consider in the prediction model include the heritability of the component traits and their genetic correlation with yield. Not all component traits are easy to measure. Therefore, it may be attractive to include proxies to yield components, where these proxies are measured in (high-throughput) phenotyping platforms during the growing season. Using the Agricultural Production Systems Simulator (APSIM)-wheat cropping systems model, we simulated phenotypes for a wheat diversity panel segregating for a set of physiological parameters regulating phenology, biomass partitioning, and the ability to capture environmental resources. The distribution of the additive quantitative trait locus effects regulating the APSIM physiological parameters approximated the same distribution of quantitative trait locus effects on real phenotypic data for yield and heading date. We use the crop growth model APSIM-wheat to simulate phenotypes in three Australian environments with contrasting water deficit patterns. The APSIM output contained the dynamics of biomass and canopy cover, plus yield at the end of the growing season. Each water deficit pattern triggered different adaptive mechanisms and the impact of component traits differed between drought scenarios. We evaluated multiple phenotyping schedules by adding plot and measurement error to the dynamics of biomass and canopy cover. We used these trait dynamics to fit parametric models and P-splines to extract parameters with a larger heritability than the phenotypes at individual time points. We used those parameters in multi-trait prediction models for final yield. The combined use of crop growth models and multi-trait genomic prediction models provides a procedure to assess the efficiency of phenotyping strategies and compare methods to model trait dynamics. It also allows us to quantify the impact of yield components on yield prediction accuracy even in different environment types. In scenarios with mild or no water stress, yield prediction accuracy benefitted from including biomass and green canopy cover parameters. The advantage of the multi-trait model was smaller for the early-drought scenario, due to the reduced correlation between the secondary and the target trait. Therefore, multi-trait genomic prediction models for yield require scenario-specific correlated traits.
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Affiliation(s)
| | - Martin P. Boer
- Biometris, Wageningen University and Research Centre, Wageningen, Netherlands
| | - Marcos Malosetti
- Biometris, Wageningen University and Research Centre, Wageningen, Netherlands
| | - Scott Chapman
- Agriculture and Food, CSIRO, Queensland Bioscience Precinct, QLD, Australia
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Toowoomba, QLD, Australia
| | - Karine Chenu
- School of Agriculture and Food Sciences, The University of Queensland, Gatton, QLD, Australia
| | - Bangyou Zheng
- Agriculture and Food, CSIRO, Queensland Bioscience Precinct, QLD, Australia
| | - Fred A. van Eeuwijk
- Biometris, Wageningen University and Research Centre, Wageningen, Netherlands
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Anderegg J, Hund A, Karisto P, Mikaberidze A. In-Field Detection and Quantification of Septoria Tritici Blotch in Diverse Wheat Germplasm Using Spectral-Temporal Features. FRONTIERS IN PLANT SCIENCE 2019; 10:1355. [PMID: 31708956 PMCID: PMC6824235 DOI: 10.3389/fpls.2019.01355] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Accepted: 10/02/2019] [Indexed: 05/26/2023]
Abstract
Hyperspectral remote sensing holds the potential to detect and quantify crop diseases in a rapid and non-invasive manner. Such tools could greatly benefit resistance breeding, but their adoption is hampered by i) a lack of specificity to disease-related effects and ii) insufficient robustness to variation in reflectance caused by genotypic diversity and varying environmental conditions, which are fundamental elements of resistance breeding. We hypothesized that relying exclusively on temporal changes in canopy reflectance during pathogenesis may allow to specifically detect and quantify crop diseases while minimizing the confounding effects of genotype and environment. To test this hypothesis, we collected time-resolved canopy hyperspectral reflectance data for 18 diverse genotypes on infected and disease-free plots and engineered spectral-temporal features representing this hypothesis. Our results confirm the lack of specificity and robustness of disease assessments based on reflectance spectra at individual time points. We show that changes in spectral reflectance over time are indicative of the presence and severity of Septoria tritici blotch (STB) infections. Furthermore, the proposed time-integrated approach facilitated the delineation of disease from physiological senescence, which is pivotal for efficient selection of STB-resistant material under field conditions. A validation of models based on spectral-temporal features on a diverse panel of 330 wheat genotypes offered evidence for the robustness of the proposed method. This study demonstrates the potential of time-resolved canopy reflectance measurements for robust assessments of foliar diseases in the context of resistance breeding.
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Affiliation(s)
- Jonas Anderegg
- Crop Science Group, Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland
| | - Andreas Hund
- Crop Science Group, Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland
| | - Petteri Karisto
- Plant Pathology Group, Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland
| | - Alexey Mikaberidze
- Plant Pathology Group, Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland
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Young SN. A Framework for Evaluating Field-Based, High-Throughput Phenotyping Systems: A Meta-Analysis. SENSORS (BASEL, SWITZERLAND) 2019; 19:E3582. [PMID: 31426499 PMCID: PMC6720174 DOI: 10.3390/s19163582] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 07/29/2019] [Accepted: 08/13/2019] [Indexed: 12/26/2022]
Abstract
This paper presents a framework for the evaluation of system complexity and utility and the identification of bottlenecks in the deployment of field-based, high-throughput phenotyping (FB-HTP) systems. Although the capabilities of technology used for high-throughput phenotyping has improved and costs decreased, there have been few, if any, successful attempts at developing turnkey field-based phenotyping systems. To identify areas for future improvement in developing turnkey FB-HTP solutions, a framework for evaluating their complexity and utility was developed and applied to total of 10 case studies to highlight potential barriers in their development and adoption. The framework performs system factorization and rates the complexity and utility of subsystem factors, as well as each FB-HTP system as a whole, and provides data related to the trends and relationships within the complexity and utility factors. This work suggests that additional research and development are needed focused around the following areas: (i) data handling and management, specifically data transfer from the field to the data processing pipeline, (ii) improved human-machine interaction to facilitate usability across multiple users, and (iii) design standardization of the factors common across all FB-HTP systems to limit the competing drivers of system complexity and utility. This framework can be used to evaluate both previously developed and future proposed systems to approximate the overall system complexity and identify areas for improvement prior to implementation.
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Affiliation(s)
- Sierra N Young
- Department of Biological and Agricultural Engineering, North Carolina State University, Raleigh, NC 27695, USA.
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Roitsch T, Cabrera-Bosquet L, Fournier A, Ghamkhar K, Jiménez-Berni J, Pinto F, Ober ES. Review: New sensors and data-driven approaches-A path to next generation phenomics. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2019; 282:2-10. [PMID: 31003608 PMCID: PMC6483971 DOI: 10.1016/j.plantsci.2019.01.011] [Citation(s) in RCA: 78] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Revised: 12/15/2018] [Accepted: 01/09/2019] [Indexed: 05/19/2023]
Abstract
At the 4th International Plant Phenotyping Symposium meeting of the International Plant Phenotyping Network (IPPN) in 2016 at CIMMYT in Mexico, a workshop was convened to consider ways forward with sensors for phenotyping. The increasing number of field applications provides new challenges and requires specialised solutions. There are many traits vital to plant growth and development that demand phenotyping approaches that are still at early stages of development or elude current capabilities. Further, there is growing interest in low-cost sensor solutions, and mobile platforms that can be transported to the experiments, rather than the experiment coming to the platform. Various types of sensors are required to address diverse needs with respect to targets, precision and ease of operation and readout. Converting data into knowledge, and ensuring that those data (and the appropriate metadata) are stored in such a way that they will be sensible and available to others now and for future analysis is also vital. Here we are proposing mechanisms for "next generation phenomics" based on our learning in the past decade, current practice and discussions at the IPPN Symposium, to encourage further thinking and collaboration by plant scientists, physicists and engineering experts.
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Affiliation(s)
- Thomas Roitsch
- Department of Plant and Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg C, Denmark; Department of Adaptive Biotechnologies, Global Change Research Institute, CAS, Brno, Czech Republic
| | | | - Antoine Fournier
- Arvalis, Institut du végétal, 45, voie Romaine 41240 Beauce la Romaine, France
| | - Kioumars Ghamkhar
- Forage Science, Grasslands Research Centre, AgResearch, Tennent Drive, Fitzherbert, Palmerston North 4410, New Zealand
| | - José Jiménez-Berni
- Instituto de Agricultura Sostenible, Consejo Superior de Investigaciones Cientificas (CSIC) Avenida Menéndez Pidal, Campus Alameda del Obispo, 14004 Córdoba, Spain
| | - Francisco Pinto
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), El Batán, Texcoco, México C.P. 56237, Mexico
| | - Eric S Ober
- National Institute of Agricultural Botany (NIAB), Huntingdon Road, Cambridge, CB3 0LE, UK.
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Krause MR, González-Pérez L, Crossa J, Pérez-Rodríguez P, Montesinos-López O, Singh RP, Dreisigacker S, Poland J, Rutkoski J, Sorrells M, Gore MA, Mondal S. Hyperspectral Reflectance-Derived Relationship Matrices for Genomic Prediction of Grain Yield in Wheat. G3 (BETHESDA, MD.) 2019; 9:1231-1247. [PMID: 30796086 PMCID: PMC6469421 DOI: 10.1534/g3.118.200856] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Accepted: 02/15/2019] [Indexed: 02/04/2023]
Abstract
Hyperspectral reflectance phenotyping and genomic selection are two emerging technologies that have the potential to increase plant breeding efficiency by improving prediction accuracy for grain yield. Hyperspectral cameras quantify canopy reflectance across a wide range of wavelengths that are associated with numerous biophysical and biochemical processes in plants. Genomic selection models utilize genome-wide marker or pedigree information to predict the genetic values of breeding lines. In this study, we propose a multi-kernel GBLUP approach to genomic selection that uses genomic marker-, pedigree-, and hyperspectral reflectance-derived relationship matrices to model the genetic main effects and genotype × environment (G × E) interactions across environments within a bread wheat (Triticum aestivum L.) breeding program. We utilized an airplane equipped with a hyperspectral camera to phenotype five differentially managed treatments of the yield trials conducted by the Bread Wheat Improvement Program of the International Maize and Wheat Improvement Center (CIMMYT) at Ciudad Obregón, México over four breeding cycles. We observed that single-kernel models using hyperspectral reflectance-derived relationship matrices performed similarly or superior to marker- and pedigree-based genomic selection models when predicting within and across environments. Multi-kernel models combining marker/pedigree information with hyperspectral reflectance phentoypes had the highest prediction accuracies; however, improvements in accuracy over marker- and pedigree-based models were marginal when correcting for days to heading. Our results demonstrate the potential of using hyperspectral imaging to predict grain yield within a multi-environment context and also support further studies on the integration of hyperspectral reflectance phenotyping into breeding programs.
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Affiliation(s)
- Margaret R Krause
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York, 14853
| | - Lorena González-Pérez
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Ciudad de México, 06600, México
| | - José Crossa
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Ciudad de México, 06600, México
| | | | | | - Ravi P Singh
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Ciudad de México, 06600, México
| | - Susanne Dreisigacker
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Ciudad de México, 06600, México
| | - Jesse Poland
- Department of Plant Pathology, Kansas State University, Manhattan, Kansas, 66506
| | - Jessica Rutkoski
- International Rice Research Institute (IRRI), DAPO Box 7777, Metro Manila, 1301, Philippines
| | - Mark Sorrells
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York, 14853
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York, 14853
| | - Suchismita Mondal
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Ciudad de México, 06600, México
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Jaiswal Y, Weber D, Yerke A, Xue Y, Lehman D, Williams T, Xiao T, Haddad D, Williams L. A substitute variety for agronomically and medicinally important Serenoa repens (saw palmetto). Sci Rep 2019; 9:4709. [PMID: 30886216 PMCID: PMC6423146 DOI: 10.1038/s41598-019-41150-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Accepted: 02/26/2019] [Indexed: 12/15/2022] Open
Abstract
Serenoa repens (saw palmetto) berries are one of the most consumed medicinal herbs in the United States and the wild green variety is used in the initial therapy of benign prostatic hyperplasia (BPH), globally. Use of saw palmetto is approved by the German Commission E, and several clinical trials are underway for evaluation of its efficacy. Exploitation of its habitats and over foraging imperil this plant, which only grows in the wild. This is the first study, to propose the use of the S. repens forma glauca (silver variety) as a qualitative substitute for the wild variety, to support its conservation. We compared tissue microstructures and lipid and water distribution through spatial imaging and examined metabolite distribution of three tissue domains and whole berries. This combined approach of 3D imaging and metabolomics provides a new strategy for studying phenotypic traits and metabolite synthesis of closely related plant varieties.
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Affiliation(s)
- Yogini Jaiswal
- Center for Excellence in Post Harvest Technologies, North Carolina Agricultural and Technical State University, The North Carolina Research Campus, 500 Laureate Way, Kannapolis, NC, 28081, USA.
| | - Daniel Weber
- Fraunhofer Development Centre X-ray Technology EZRT, a Division of Fraunhofer Institute for Integrated Circuits IIS, Department Magnetic Resonance and X-ray Imaging MRB, Am Hubland D-97074, Wuerzburg, Germany
| | - Aaron Yerke
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, North Carolina, 28223, USA
| | - Yanling Xue
- Shanghai Advanced Research Institute, Chinese Academy of Sciences, Pudong District, Shanghai, 201203, P. R. China.,Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Pudong District, Shanghai, 201203, P. R. China
| | - Danielle Lehman
- Mass Spectrometry Facility, Department of Chemistry, North Carolina State University 2620 Yarbrough Drive, Campus Box 8204, Raleigh, NC, 27695, USA
| | - Taufika Williams
- Mass Spectrometry Facility, Department of Chemistry, North Carolina State University 2620 Yarbrough Drive, Campus Box 8204, Raleigh, NC, 27695, USA
| | - Tiqiao Xiao
- Shanghai Advanced Research Institute, Chinese Academy of Sciences, Pudong District, Shanghai, 201203, P. R. China.,Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Pudong District, Shanghai, 201203, P. R. China
| | - Daniel Haddad
- Fraunhofer Development Centre X-ray Technology EZRT, a Division of Fraunhofer Institute for Integrated Circuits IIS, Department Magnetic Resonance and X-ray Imaging MRB, Am Hubland D-97074, Wuerzburg, Germany
| | - Leonard Williams
- Center for Excellence in Post Harvest Technologies, North Carolina Agricultural and Technical State University, The North Carolina Research Campus, 500 Laureate Way, Kannapolis, NC, 28081, USA.
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40
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Rosenqvist E, Großkinsky DK, Ottosen CO, van de Zedde R. The Phenotyping Dilemma-The Challenges of a Diversified Phenotyping Community. FRONTIERS IN PLANT SCIENCE 2019; 10:163. [PMID: 30873188 PMCID: PMC6403123 DOI: 10.3389/fpls.2019.00163] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Accepted: 01/30/2019] [Indexed: 05/08/2023]
Affiliation(s)
- Eva Rosenqvist
- Department of Plant and Environmental Sciences, University of Copenhagen, Taastrup, Denmark
| | - Dominik K. Großkinsky
- Department of Plant and Environmental Sciences, Copenhagen Plant Science Centre, University of Copenhagen, Frederiksberg, Denmark
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Oyiga BC, Ogbonnaya FC, Sharma RC, Baum M, Léon J, Ballvora A. Genetic and transcriptional variations in NRAMP-2 and OPAQUE1 genes are associated with salt stress response in wheat. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2019; 132:323-346. [PMID: 30392081 PMCID: PMC6349800 DOI: 10.1007/s00122-018-3220-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Accepted: 10/24/2018] [Indexed: 05/02/2023]
Abstract
SNP alleles on chromosomes 4BL and 6AL are associated with sensitivity to salt tolerance in wheat and upon validation can be exploited in the development of salt-tolerant wheat varieties. The dissection of the genetic and molecular components of salt stress response offers strong opportunities toward understanding and improving salt tolerance in crops. In this study, GWAS was employed to identify a total of 106 SNP loci (R2 = 0.12-63.44%) linked to salt stress response in wheat using leaf chlorophyll fluorescence, grain quality and shoot ionic (Na+ and K+ ions) attributes. Among them, 14 SNP loci individually conferred pleiotropic effects on multiple independent salinity tolerance traits including loci at 99.04 cM (R2 ≥ 14.7%) and 68.45 cM (R2 ≥ 4.10%) on chromosomes 6AL and 4BL, respectively, that influenced shoot Na+-uptake, shoot K+/Na+ ratio, and specific energy fluxes for absorption (ABS/RC) and dissipation (DIo/RC). Analysis of the open reading frame (ORF) containing the SNP markers revealed that they are orthologous to genes involved in photosynthesis and plant stress (salt) response. Further transcript abundance and qRT-PCR analyses indicated that the genes are mostly up-regulated in salt-tolerant and down-regulated in salt-sensitive wheat genotypes including NRAMP-2 and OPAQUE1 genes on 4BL and 6AL, respectively. Both genes showed highest differential expression between contrasting genotypes when expressions of all the genes within their genetic intervals were analyzed. Possible cis-acting regulatory elements and coding sequence variation that may be involved in salt stress response were also identified in both genes. This study identified genetic and molecular components of salt stress response that are associated with Na+-uptake, shoot Na+/K+ ratio, ABS/RC, DIo/RC, and grain quality traits and upon functional validation would facilitate the development of gene-specific markers that could be deployed to improve salinity tolerance in wheat.
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Affiliation(s)
- Benedict C Oyiga
- INRES-Pflanzenzuchtung, Rheinische Friedrich-Wilhelms-Universitat, Bonn, Germany
- Center for Development Research (ZEF), Rheinische Friedrich-Wilhelms-Universitat, Bonn, Germany
| | | | - Ram C Sharma
- International Center for Agricultural Research in the Dry Areas (ICARDA), Tashkent, Uzbekistan
| | - Michael Baum
- International Centre for Agricultural Research in the Dry Areas (ICARDA), Al Irfane, 10112, Rabat, Morocco
| | - Jens Léon
- INRES-Pflanzenzuchtung, Rheinische Friedrich-Wilhelms-Universitat, Bonn, Germany
| | - Agim Ballvora
- INRES-Pflanzenzuchtung, Rheinische Friedrich-Wilhelms-Universitat, Bonn, Germany.
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Bolger AM, Poorter H, Dumschott K, Bolger ME, Arend D, Osorio S, Gundlach H, Mayer KFX, Lange M, Scholz U, Usadel B. Computational aspects underlying genome to phenome analysis in plants. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2019; 97:182-198. [PMID: 30500991 PMCID: PMC6849790 DOI: 10.1111/tpj.14179] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Revised: 11/06/2018] [Accepted: 11/16/2018] [Indexed: 05/18/2023]
Abstract
Recent advances in genomics technologies have greatly accelerated the progress in both fundamental plant science and applied breeding research. Concurrently, high-throughput plant phenotyping is becoming widely adopted in the plant community, promising to alleviate the phenotypic bottleneck. While these technological breakthroughs are significantly accelerating quantitative trait locus (QTL) and causal gene identification, challenges to enable even more sophisticated analyses remain. In particular, care needs to be taken to standardize, describe and conduct experiments robustly while relying on plant physiology expertise. In this article, we review the state of the art regarding genome assembly and the future potential of pangenomics in plant research. We also describe the necessity of standardizing and describing phenotypic studies using the Minimum Information About a Plant Phenotyping Experiment (MIAPPE) standard to enable the reuse and integration of phenotypic data. In addition, we show how deep phenotypic data might yield novel trait-trait correlations and review how to link phenotypic data to genomic data. Finally, we provide perspectives on the golden future of machine learning and their potential in linking phenotypes to genomic features.
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Affiliation(s)
- Anthony M. Bolger
- Institute for Biology I, BioSCRWTH Aachen UniversityWorringer Weg 352074AachenGermany
| | - Hendrik Poorter
- Forschungszentrum Jülich (FZJ) Institute of Bio‐ and Geosciences (IBG‐2) Plant SciencesWilhelm‐Johnen‐Straße52428JülichGermany
- Department of Biological SciencesMacquarie UniversityNorth RydeNSW2109Australia
| | - Kathryn Dumschott
- Institute for Biology I, BioSCRWTH Aachen UniversityWorringer Weg 352074AachenGermany
| | - Marie E. Bolger
- Forschungszentrum Jülich (FZJ) Institute of Bio‐ and Geosciences (IBG‐2) Plant SciencesWilhelm‐Johnen‐Straße52428JülichGermany
| | - Daniel Arend
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) GaterslebenCorrensstraße 306466SeelandGermany
| | - Sonia Osorio
- Department of Molecular Biology and BiochemistryInstituto de Hortofruticultura Subtropical y Mediterránea “La Mayora”Universidad de Málaga‐Consejo Superior de Investigaciones CientíficasCampus de Teatinos29071MálagaSpain
| | - Heidrun Gundlach
- Plant Genome and Systems Biology (PGSB)Helmholtz Zentrum München (HMGU)Ingolstädter Landstraße 185764NeuherbergGermany
| | - Klaus F. X. Mayer
- Plant Genome and Systems Biology (PGSB)Helmholtz Zentrum München (HMGU)Ingolstädter Landstraße 185764NeuherbergGermany
| | - Matthias Lange
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) GaterslebenCorrensstraße 306466SeelandGermany
| | - Uwe Scholz
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) GaterslebenCorrensstraße 306466SeelandGermany
| | - Björn Usadel
- Institute for Biology I, BioSCRWTH Aachen UniversityWorringer Weg 352074AachenGermany
- Forschungszentrum Jülich (FZJ) Institute of Bio‐ and Geosciences (IBG‐2) Plant SciencesWilhelm‐Johnen‐Straße52428JülichGermany
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43
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Bray AL, Topp CN. The Quantitative Genetic Control of Root Architecture in Maize. PLANT & CELL PHYSIOLOGY 2018; 59:1919-1930. [PMID: 30020530 PMCID: PMC6178961 DOI: 10.1093/pcp/pcy141] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Accepted: 07/04/2018] [Indexed: 05/07/2023]
Abstract
Roots remain an underexplored frontier in plant genetics despite their well-known influence on plant development, agricultural performance and competition in the wild. Visualizing and measuring root structures and their growth is vastly more difficult than characterizing aboveground parts of the plant and is often simply avoided. The majority of research on maize root systems has focused on their anatomy, physiology, development and soil interaction, but much less is known about the genetics that control quantitative traits. In maize, seven root development genes have been cloned using mutagenesis, but no genes underlying the many root-related quantitative trait loci (QTLs) have been identified. In this review, we discuss whether the maize mutants known to control root development may also influence quantitative aspects of root architecture, including the extent to which they overlap with the most recent maize root trait QTLs. We highlight specific challenges and anticipate the impacts that emerging technologies, especially computational approaches, may have toward the identification of genes controlling root quantitative traits.
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Affiliation(s)
- Adam L Bray
- Division of Plant Sciences, University of Missouri-Columbia, Columbia, MO, USA
- Donald Danforth Plant Science Center, St. Louis, MO, USA
| | - Christopher N Topp
- Donald Danforth Plant Science Center, St. Louis, MO, USA
- Corresponding author: E-mail, ; Fax, 314 587 1501
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AlKhalifah N, Campbell DA, Falcon CM, Gardiner JM, Miller ND, Romay MC, Walls R, Walton R, Yeh CT, Bohn M, Bubert J, Buckler ES, Ciampitti I, Flint-Garcia S, Gore MA, Graham C, Hirsch C, Holland JB, Hooker D, Kaeppler S, Knoll J, Lauter N, Lee EC, Lorenz A, Lynch JP, Moose SP, Murray SC, Nelson R, Rocheford T, Rodriguez O, Schnable JC, Scully B, Smith M, Springer N, Thomison P, Tuinstra M, Wisser RJ, Xu W, Ertl D, Schnable PS, De Leon N, Spalding EP, Edwards J, Lawrence-Dill CJ. Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets. BMC Res Notes 2018; 11:452. [PMID: 29986751 PMCID: PMC6038255 DOI: 10.1186/s13104-018-3508-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Accepted: 06/18/2018] [Indexed: 11/10/2022] Open
Abstract
OBJECTIVES Crop improvement relies on analysis of phenotypic, genotypic, and environmental data. Given large, well-integrated, multi-year datasets, diverse queries can be made: Which lines perform best in hot, dry environments? Which alleles of specific genes are required for optimal performance in each environment? Such datasets also can be leveraged to predict cultivar performance, even in uncharacterized environments. The maize Genomes to Fields (G2F) Initiative is a multi-institutional organization of scientists working to generate and analyze such datasets from existing, publicly available inbred lines and hybrids. G2F's genotype by environment project has released 2014 and 2015 datasets to the public, with 2016 and 2017 collected and soon to be made available. DATA DESCRIPTION Datasets include DNA sequences; traditional phenotype descriptions, as well as detailed ear, cob, and kernel phenotypes quantified by image analysis; weather station measurements; and soil characterizations by site. Data are released as comma separated value spreadsheets accompanied by extensive README text descriptions. For genotypic and phenotypic data, both raw data and a version with outliers removed are reported. For weather data, two versions are reported: a full dataset calibrated against nearby National Weather Service sites and a second calibrated set with outliers and apparent artifacts removed.
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Affiliation(s)
- Naser AlKhalifah
- Iowa State University, Ames, IA 50011 USA
- Present Address: University of Wisconsin, Madison, WI 53706 USA
| | | | | | - Jack M. Gardiner
- Iowa State University, Ames, IA 50011 USA
- Present Address: University of Missouri, Columbia, MO 65211 USA
| | | | | | | | | | | | - Martin Bohn
- University of Illinois at Urbana-Champaign, Urbana, IL 61801 USA
| | - Jessica Bubert
- University of Illinois at Urbana-Champaign, Urbana, IL 61801 USA
| | - Edward S. Buckler
- Cornell University, Ithaca, NY 14853 USA
- USDA-ARS, Beltsville, MD USA
| | | | | | | | | | | | - James B. Holland
- USDA-ARS, Beltsville, MD USA
- North Carolina State University, Raleigh, NC 27695 USA
| | | | | | | | - Nick Lauter
- Iowa State University, Ames, IA 50011 USA
- USDA-ARS, Beltsville, MD USA
| | | | - Aaron Lorenz
- University of Nebraska, Lincoln, NE 68583 USA
- Present Address: University of Minnesota, St. Paul, MN 55108 USA
| | | | - Stephen P. Moose
- University of Illinois at Urbana-Champaign, Urbana, IL 61801 USA
| | | | | | | | | | | | - Brian Scully
- USDA-ARS, Beltsville, MD USA
- University of Florida, Gainesville, FL 32611 USA
| | | | | | | | | | | | - Wenwei Xu
- Texas A&M AgriLife Research, Lubbock, TX 79403 USA
| | - David Ertl
- Iowa Corn Growers Association, Johnston, IA 50131 USA
| | | | | | | | - Jode Edwards
- Iowa State University, Ames, IA 50011 USA
- USDA-ARS, Beltsville, MD USA
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Araus JL, Kefauver SC, Zaman-Allah M, Olsen MS, Cairns JE. Translating High-Throughput Phenotyping into Genetic Gain. TRENDS IN PLANT SCIENCE 2018; 23:451-466. [PMID: 29555431 PMCID: PMC5931794 DOI: 10.1016/j.tplants.2018.02.001] [Citation(s) in RCA: 269] [Impact Index Per Article: 44.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2017] [Revised: 01/23/2018] [Accepted: 02/01/2018] [Indexed: 05/18/2023]
Abstract
Inability to efficiently implement high-throughput field phenotyping is increasingly perceived as a key component that limits genetic gain in breeding programs. Field phenotyping must be integrated into a wider context than just choosing the correct selection traits, deployment tools, evaluation platforms, or basic data-management methods. Phenotyping means more than conducting such activities in a resource-efficient manner; it also requires appropriate trial management and spatial variability handling, definition of key constraining conditions prevalent in the target population of environments, and the development of more comprehensive data management, including crop modeling. This review will provide a wide perspective on how field phenotyping is best implemented. It will also outline how to bridge the gap between breeders and 'phenotypers' in an effective manner.
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Affiliation(s)
- José Luis Araus
- Unit of Plant Physiology, Faculty of Biology, University of Barcelona, Barcelona, Spain.
| | - Shawn C Kefauver
- Unit of Plant Physiology, Faculty of Biology, University of Barcelona, Barcelona, Spain
| | - Mainassara Zaman-Allah
- Global Maize Program, International Maize and Wheat Improvement Center (CIMMYT) Southern Africa Regional Office, Harare, Zimbabwe
| | | | - Jill E Cairns
- Global Maize Program, International Maize and Wheat Improvement Center (CIMMYT) Southern Africa Regional Office, Harare, Zimbabwe
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Pauli D, Ziegler G, Ren M, Jenks MA, Hunsaker DJ, Zhang M, Baxter I, Gore MA. Multivariate Analysis of the Cotton Seed Ionome Reveals a Shared Genetic Architecture. G3 (BETHESDA, MD.) 2018; 8:1147-1160. [PMID: 29437829 PMCID: PMC5873906 DOI: 10.1534/g3.117.300479] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2017] [Accepted: 01/28/2018] [Indexed: 02/01/2023]
Abstract
To mitigate the effects of heat and drought stress, a better understanding of the genetic control of physiological responses to these environmental conditions is needed. To this end, we evaluated an upland cotton (Gossypium hirsutum L.) mapping population under water-limited and well-watered conditions in a hot, arid environment. The elemental concentrations (ionome) of seed samples from the population were profiled in addition to those of soil samples taken from throughout the field site to better model environmental variation. The elements profiled in seeds exhibited moderate to high heritabilities, as well as strong phenotypic and genotypic correlations between elements that were not altered by the imposed irrigation regimes. Quantitative trait loci (QTL) mapping results from a Bayesian classification method identified multiple genomic regions where QTL for individual elements colocalized, suggesting that genetic control of the ionome is highly interrelated. To more fully explore this genetic architecture, multivariate QTL mapping was implemented among groups of biochemically related elements. This analysis revealed both additional and pleiotropic QTL responsible for coordinated control of phenotypic variation for elemental accumulation. Machine learning algorithms that utilized only ionomic data predicted the irrigation regime under which genotypes were evaluated with very high accuracy. Taken together, these results demonstrate the extent to which the seed ionome is genetically interrelated and predictive of plant physiological responses to adverse environmental conditions.
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Affiliation(s)
- Duke Pauli
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853
| | - Greg Ziegler
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132
- United States Department of Agriculture-Agricultural Research Service (USDA-ARS), Plant Genetics Research Unit, St. Louis, Missouri 63132
| | - Min Ren
- Department of Statistics, Purdue University, West Lafayette, Indiana 47907
| | - Matthew A Jenks
- Division of Plant and Soil Sciences, West Virginia University, Morgantown, West Virginia 26506, and
| | | | - Min Zhang
- Department of Statistics, Purdue University, West Lafayette, Indiana 47907
| | - Ivan Baxter
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132
- United States Department of Agriculture-Agricultural Research Service (USDA-ARS), Plant Genetics Research Unit, St. Louis, Missouri 63132
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853,
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47
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Leaf and Canopy Level Detection of Fusarium Virguliforme (Sudden Death Syndrome) in Soybean. REMOTE SENSING 2018. [DOI: 10.3390/rs10030426] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Chen X, Li Y, He R, Ding Q. Phenotyping field-state wheat root system architecture for root foraging traits in response to environment×management interactions. Sci Rep 2018; 8:2642. [PMID: 29422488 PMCID: PMC5805786 DOI: 10.1038/s41598-018-20361-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2017] [Accepted: 01/16/2018] [Indexed: 01/26/2023] Open
Abstract
An important aspect of below-ground crop physiology is its root foraging performance, which is inherently related to root system architecture (RSA). A 2-yr field experiment was conducted and the field-state wheat RSA was phenotyped for root foraging trait (RFT). Four RSA-derived traits, i.e. Root horizontal angle (RHA), axial root expansion volume (AREV), RSA convex hull volume (CHV) and effective volume per unit root length (EVURL), were analyzed for RFTs in response to environment × management interactions. Results showed a dynamical RHA process but without statistical difference both within crop seasons and tillage treatments. AREV increased with root developmental stages, revealing an overall better root performance in the first year. However, tillage treatments did not induce observed difference within both crop seasons. CHV varied drastically from year to year and between tillage treatments, correlating well to the root length, but not with RHA. EVURL was both sensitive to tillage treatments and crop seasons, being a potential indicator for RFT. Above all, tillage effect on RFT was statistically far less than that induced by crop seasons. Pro/E assisted modeling can be used as an effective means for phenotyping integrated, RSA-derived, RFTs for root foraging response to induced environment × management interactions.
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Affiliation(s)
- Xinxin Chen
- College of Engineering, Nanjing Agricultural University/Key Laboratory of Intelligent Agricultural Equipment of Jiangsu Province, Nanjing, 210031, China
| | - Yinian Li
- College of Engineering, Nanjing Agricultural University/Key Laboratory of Intelligent Agricultural Equipment of Jiangsu Province, Nanjing, 210031, China
| | - Ruiyin He
- College of Engineering, Nanjing Agricultural University/Key Laboratory of Intelligent Agricultural Equipment of Jiangsu Province, Nanjing, 210031, China
| | - Qishuo Ding
- College of Engineering, Nanjing Agricultural University/Key Laboratory of Intelligent Agricultural Equipment of Jiangsu Province, Nanjing, 210031, China.
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Jiang Y, Li C, Paterson AH, Sun S, Xu R, Robertson J. Quantitative Analysis of Cotton Canopy Size in Field Conditions Using a Consumer-Grade RGB-D Camera. FRONTIERS IN PLANT SCIENCE 2018; 8:2233. [PMID: 29441074 PMCID: PMC5797632 DOI: 10.3389/fpls.2017.02233] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Accepted: 12/19/2017] [Indexed: 05/31/2023]
Abstract
Plant canopy structure can strongly affect crop functions such as yield and stress tolerance, and canopy size is an important aspect of canopy structure. Manual assessment of canopy size is laborious and imprecise, and cannot measure multi-dimensional traits such as projected leaf area and canopy volume. Field-based high throughput phenotyping systems with imaging capabilities can rapidly acquire data about plants in field conditions, making it possible to quantify and monitor plant canopy development. The goal of this study was to develop a 3D imaging approach to quantitatively analyze cotton canopy development in field conditions. A cotton field was planted with 128 plots, including four genotypes of 32 plots each. The field was scanned by GPhenoVision (a customized field-based high throughput phenotyping system) to acquire color and depth images with GPS information in 2016 covering two growth stages: canopy development, and flowering and boll development. A data processing pipeline was developed, consisting of three steps: plot point cloud reconstruction, plant canopy segmentation, and trait extraction. Plot point clouds were reconstructed using color and depth images with GPS information. In colorized point clouds, vegetation was segmented from the background using an excess-green (ExG) color filter, and cotton canopies were further separated from weeds based on height, size, and position information. Static morphological traits were extracted on each day, including univariate traits (maximum and mean canopy height and width, projected canopy area, and concave and convex volumes) and a multivariate trait (cumulative height profile). Growth rates were calculated for univariate static traits, quantifying canopy growth and development. Linear regressions were performed between the traits and fiber yield to identify the best traits and measurement time for yield prediction. The results showed that fiber yield was correlated with static traits after the canopy development stage (R2 = 0.35-0.71) and growth rates in early canopy development stages (R2 = 0.29-0.52). Multi-dimensional traits (e.g., projected canopy area and volume) outperformed one-dimensional traits, and the multivariate trait (cumulative height profile) outperformed univariate traits. The proposed approach would be useful for identification of quantitative trait loci (QTLs) controlling canopy size in genetics/genomics studies or for fiber yield prediction in breeding programs and production environments.
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Affiliation(s)
- Yu Jiang
- Bio-sensing and Instrumentation Laboratory, School of Electrical and Computer Engineering, College of Engineering, University of Georgia, Athens, GA, United States
| | - Changying Li
- Bio-sensing and Instrumentation Laboratory, School of Electrical and Computer Engineering, College of Engineering, University of Georgia, Athens, GA, United States
| | - Andrew H. Paterson
- Plant Genome Mapping Laboratory, College of Agricultural and Environmental Sciences, University of Georgia, Athens, GA, United States
- Department of Genetics, Franklin College of Arts and Sciences, University of Georgia, Athens, GA, United States
| | - Shangpeng Sun
- Bio-sensing and Instrumentation Laboratory, School of Electrical and Computer Engineering, College of Engineering, University of Georgia, Athens, GA, United States
| | - Rui Xu
- Bio-sensing and Instrumentation Laboratory, School of Electrical and Computer Engineering, College of Engineering, University of Georgia, Athens, GA, United States
| | - Jon Robertson
- Plant Genome Mapping Laboratory, College of Agricultural and Environmental Sciences, University of Georgia, Athens, GA, United States
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50
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Gehan MA, Fahlgren N, Abbasi A, Berry JC, Callen ST, Chavez L, Doust AN, Feldman MJ, Gilbert KB, Hodge JG, Hoyer JS, Lin A, Liu S, Lizárraga C, Lorence A, Miller M, Platon E, Tessman M, Sax T. PlantCV v2: Image analysis software for high-throughput plant phenotyping. PeerJ 2017; 5:e4088. [PMID: 29209576 PMCID: PMC5713628 DOI: 10.7717/peerj.4088] [Citation(s) in RCA: 110] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2017] [Accepted: 11/03/2017] [Indexed: 12/11/2022] Open
Abstract
Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.
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Affiliation(s)
- Malia A. Gehan
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
| | - Noah Fahlgren
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
| | - Arash Abbasi
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
| | - Jeffrey C. Berry
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
| | - Steven T. Callen
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
- Current affiliation: Monsanto Company, St. Louis, MO, United States of America
| | - Leonardo Chavez
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
| | - Andrew N. Doust
- Department of Plant Biology, Ecology, and Evolution, Oklahoma State University, Stillwater, OK, United States of America
| | - Max J. Feldman
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
| | - Kerrigan B. Gilbert
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
| | - John G. Hodge
- Department of Plant Biology, Ecology, and Evolution, Oklahoma State University, Stillwater, OK, United States of America
| | - J. Steen Hoyer
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
- Computational and Systems Biology Program, Washington University in St. Louis, St. Louis, MO, United States of America
| | - Andy Lin
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
- Current affiliation: Unidev, St. Louis, MO, United States of America
| | - Suxing Liu
- Arkansas Biosciences Institute, Arkansas State University, Jonesboro, AR, United States of America
- Current affiliation: Department of Plant Biology, University of Georgia, Athens, GA, United States of America
| | - César Lizárraga
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
- Current affiliation: CiBO Technologies, Cambridge, MA, United States of America
| | - Argelia Lorence
- Arkansas Biosciences Institute, Department of Chemistry and Physics, Arkansas State University, Jonesboro, AR, United States of America
| | - Michael Miller
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
- Current affiliation: Department of Agronomy and Horticulture, Center for Plant Science Innovation, Beadle Center for Biotechnology, University of Nebraska - Lincoln, Lincoln, NE, United States of America
| | | | - Monica Tessman
- Donald Danforth Plant Science Center, St. Louis, MO, United States of America
- Department of Plant Biology, Ecology, and Evolution, Oklahoma State University, Stillwater, OK, United States of America
| | - Tony Sax
- Missouri University of Science and Technology, Rolla, MO, United States of America
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