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您的检索式：作者名="Patrick SSchnable"
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| 1 | An integrated framework reinstating the environmental dimension for GWAS and genomic selection in crops显示文摘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. | Xianran Li Tingting Guo Jinyu Wang Wubishet ABekele Sivakumar Sukumaran Adam EVanous James PMcNellie Laura Tibbs Cortes Marta SLopes Kendall RLamkey Mark EWestgate John KMcKay Sotirios VArchontoulis Matthew PReynolds Nicholas ATinker Patrick SSchnable Jianming Yu | 2021 | Molecular Plant2021，14，6： | 0 |
| 2 | Detection of the Progression of Anthesis in Field-Grown Maize Tassels:A Case Study显示文摘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. | Seyed Vahid Mirnezami Srikant Srinivasan Yan Zhou Patrick SSchnable Baskar Ganapathysubramanian | 2021 | Plant Phenomics2021，3，1： | 0 |
| 3 | KAT4IA:K-Means Assisted Training for Image Analysis of Field-Grown Plant Phenotypes显示文摘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. | Xingche Guo Yumou Qiu Dan Nettleton Cheng-Ting Yeh Zihao Zheng Stefan Hey Patrick SSchnable | 2021 | Plant Phenomics2021，3，1： | 0 |
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