Identifying mechanisms and pathways involved in gene–environment interplay and phenotypic plasticity is a long-standing challenge.It is highly desirable to establish an integrated framework with an environmental dime...Identifying mechanisms and pathways involved in gene–environment interplay and phenotypic plasticity is a long-standing challenge.It is highly desirable to establish an integrated framework with an environmental dimension for complex trait dissection and prediction.A critical step is to identify an environmental index that is both biologically relevant and estimable for new environments.With extensive field-observed complex traits,environmental profiles,and genome-wide single nucleotide polymorphisms for three major crops(maize,wheat,and oat),we demonstrated that identifying such an environmental index(i.e.,a combination of environmental parameter and growth window)enables genome-wide association studies and genomic selection of complex traits to be conducted with an explicit environmental dimension.Interestingly,genes identified for two reaction-norm parameters(i.e.,intercept and slope)derived from flowering time values along the environmental index were less colocalized for a diverse maize panel than for wheat and oat breeding panels,agreeing with the different diversity levels and genetic constitutions of the panels.In addition,we showcased the usefulness of this framework for systematically forecasting the performance of diverse germplasm panels in new environments.This general framework and the companion CERIS-JGRA analytical package should facilitate biologically informed dissection of complex traits,enhanced performance prediction in breeding for future climates,and coordinated efforts to enrich our understanding of mechanisms underlying phenotypic variation.展开更多
High-throughput phenotyping enables the efficient collection of plant trait data at scale.One example involves using imaging systems over key phases of a crop growing season.Although the resulting images provide rich ...High-throughput phenotyping enables the efficient collection of plant trait data at scale.One example involves using imaging systems over key phases of a crop growing season.Although the resulting images provide rich data for statistical analyses of plant phenotypes,image processing for trait extraction is required as a prerequisite.Current methods for trait extraction are mainly based on supervised learning with human labeled data or semisupervised learning with a mixture of human labeled data and unsupervised data.Unfortunately,preparing a sufficiently large training data is both time and labor-intensive.We describe a self-supervised pipeline(KAT4IA)that uses K-means clustering on greenhouse images to construct training data for extracting and analyzing plant traits from an image-based field phenotyping system.The KAT4IA pipeline includes these main steps:self-supervised training set construction,plant segmentation from images of field-grown plants,automatic separation of target plants,calculation of plant traits,and functional curve fitting of the extracted traits.To deal with the challenge of separating target plants from noisy backgrounds in field images,we describe a novel approach using row-cuts and column-cuts on images segmented by transform domain neural network learning,which utilizes plant pixels identified from greenhouse images to train a segmentation model for field images.This approach is efficient and does not require human intervention.Our results show that KAT4IA is able to accurately extract plant pixels and estimate plant heights.展开更多
The tassel of the maize plant is responsible for the production and dispersal of pollen for subsequent capture by the silk(stigma)and fertilization of the ovules.Both the amount and timing of pollen shed are physiolog...The tassel of the maize plant is responsible for the production and dispersal of pollen for subsequent capture by the silk(stigma)and fertilization of the ovules.Both the amount and timing of pollen shed are physiological traits that impact the production of a hybrid seed.This study describes an automated end-to-end pipeline that combines deep learning and image processing approaches to extract tassel flowering patterns from time-lapse camera images of plants grown under field conditions.Inbred lines from the SAM and NAM diversity panels were grown at the Curtiss farm at Iowa State University,Ames,IA,during the summer of 2016.Using a set of around 500 pole-mounted cameras installed in the field,images of plants were captured every 10 minutes of daylight hours over a three-week period.Extracting data from imaging performed under field conditions is challenging due to variabilities in weather,illumination,and the morphological diversity of tassels.To address these issues,deep learning algorithms were used for tassel detection,classification,and segmentation.Image processing approaches were then used to crop the main spike of the tassel to track reproductive development.The results demonstrated that deep learning with well-labeled data is a powerful tool for detecting,classifying,and segmenting tassels.Our sequential workflow exhibited the following metrics:mAP for tassel detection was 0.91,F1 score obtained for tassel classification was 0.93,and accuracy of semantic segmentation in creating a binary image from the RGB tassel images was 0.95.This workflow was used to determine spatiotemporal variations in the thickness of the main spike—which serves as a proxy for anthesis progression.展开更多
基金supported by the Agriculture and Food Research Initiative competitive grant(2021-67013-33833)the USDA National Institute of Food and Agriculture,the Advanced Research Projects Agency-Energy program(DEAR0000826)+1 种基金the Department of Energy,the National Science Foundation(IOS-1546657)the Iowa State University Ray-mond F.Baker Center for Plant Breeding,and the Iowa State University Plant Sciences Institute.
文摘Identifying mechanisms and pathways involved in gene–environment interplay and phenotypic plasticity is a long-standing challenge.It is highly desirable to establish an integrated framework with an environmental dimension for complex trait dissection and prediction.A critical step is to identify an environmental index that is both biologically relevant and estimable for new environments.With extensive field-observed complex traits,environmental profiles,and genome-wide single nucleotide polymorphisms for three major crops(maize,wheat,and oat),we demonstrated that identifying such an environmental index(i.e.,a combination of environmental parameter and growth window)enables genome-wide association studies and genomic selection of complex traits to be conducted with an explicit environmental dimension.Interestingly,genes identified for two reaction-norm parameters(i.e.,intercept and slope)derived from flowering time values along the environmental index were less colocalized for a diverse maize panel than for wheat and oat breeding panels,agreeing with the different diversity levels and genetic constitutions of the panels.In addition,we showcased the usefulness of this framework for systematically forecasting the performance of diverse germplasm panels in new environments.This general framework and the companion CERIS-JGRA analytical package should facilitate biologically informed dissection of complex traits,enhanced performance prediction in breeding for future climates,and coordinated efforts to enrich our understanding of mechanisms underlying phenotypic variation.
基金the US National Science Foundation under grant HDR:TRIPODS 19-34884the United States Department of Agriculture National Institute of Food and Agriculture Hatch project IOW03617,the Office of Science(BER),U.S.Department of Energy,Grant no.DE-SC0020355the Plant Sciences Institute,Iowa State University,Scholars Program.
文摘High-throughput phenotyping enables the efficient collection of plant trait data at scale.One example involves using imaging systems over key phases of a crop growing season.Although the resulting images provide rich data for statistical analyses of plant phenotypes,image processing for trait extraction is required as a prerequisite.Current methods for trait extraction are mainly based on supervised learning with human labeled data or semisupervised learning with a mixture of human labeled data and unsupervised data.Unfortunately,preparing a sufficiently large training data is both time and labor-intensive.We describe a self-supervised pipeline(KAT4IA)that uses K-means clustering on greenhouse images to construct training data for extracting and analyzing plant traits from an image-based field phenotyping system.The KAT4IA pipeline includes these main steps:self-supervised training set construction,plant segmentation from images of field-grown plants,automatic separation of target plants,calculation of plant traits,and functional curve fitting of the extracted traits.To deal with the challenge of separating target plants from noisy backgrounds in field images,we describe a novel approach using row-cuts and column-cuts on images segmented by transform domain neural network learning,which utilizes plant pixels identified from greenhouse images to train a segmentation model for field images.This approach is efficient and does not require human intervention.Our results show that KAT4IA is able to accurately extract plant pixels and estimate plant heights.
基金We acknowl-edge partial support from NSF(1842097)USDA NIFA(2020-68013-30934,2020-67021-31528).
文摘The tassel of the maize plant is responsible for the production and dispersal of pollen for subsequent capture by the silk(stigma)and fertilization of the ovules.Both the amount and timing of pollen shed are physiological traits that impact the production of a hybrid seed.This study describes an automated end-to-end pipeline that combines deep learning and image processing approaches to extract tassel flowering patterns from time-lapse camera images of plants grown under field conditions.Inbred lines from the SAM and NAM diversity panels were grown at the Curtiss farm at Iowa State University,Ames,IA,during the summer of 2016.Using a set of around 500 pole-mounted cameras installed in the field,images of plants were captured every 10 minutes of daylight hours over a three-week period.Extracting data from imaging performed under field conditions is challenging due to variabilities in weather,illumination,and the morphological diversity of tassels.To address these issues,deep learning algorithms were used for tassel detection,classification,and segmentation.Image processing approaches were then used to crop the main spike of the tassel to track reproductive development.The results demonstrated that deep learning with well-labeled data is a powerful tool for detecting,classifying,and segmenting tassels.Our sequential workflow exhibited the following metrics:mAP for tassel detection was 0.91,F1 score obtained for tassel classification was 0.93,and accuracy of semantic segmentation in creating a binary image from the RGB tassel images was 0.95.This workflow was used to determine spatiotemporal variations in the thickness of the main spike—which serves as a proxy for anthesis progression.
