Shakoor Nadia, Lee Scott, Mockler Todd C
Donald Danforth Plant Science Center, United States.
Donald Danforth Plant Science Center, United States.
Curr Opin Plant Biol. 2017 Aug;38:184-192. doi: 10.1016/j.pbi.2017.05.006. Epub 2017 Jul 21.
Effective implementation of technology that facilitates accurate and high-throughput screening of thousands of field-grown lines is critical for accelerating crop improvement and breeding strategies for higher yield and disease tolerance. Progress in the development of field-based high throughput phenotyping methods has advanced considerably in the last 10 years through technological progress in sensor development and high-performance computing. Here, we review recent advances in high throughput field phenotyping technologies designed to inform the genetics of quantitative traits, including crop yield and disease tolerance. Successful application of phenotyping platforms to advance crop breeding and identify and monitor disease requires: (1) high resolution of imaging and environmental sensors; (2) quality data products that facilitate computer vision, machine learning and GIS; (3) capacity infrastructure for data management and analysis; and (4) automated environmental data collection. Accelerated breeding for agriculturally relevant crop traits is key to the development of improved varieties and is critically dependent on high-resolution, high-throughput field-scale phenotyping technologies that can efficiently discriminate better performing lines within a larger population and across multiple environments.
有效实施能够促进对数千个田间种植品系进行准确且高通量筛选的技术,对于加速作物改良以及提高产量和抗病性的育种策略至关重要。在过去十年中,基于田间的高通量表型分析方法的发展取得了显著进展,这得益于传感器开发和高性能计算方面的技术进步。在此,我们回顾旨在为数量性状遗传学提供信息的高通量田间表型分析技术的最新进展,包括作物产量和抗病性。将表型分析平台成功应用于推进作物育种以及识别和监测病害需要:(1)成像和环境传感器的高分辨率;(2)便于计算机视觉、机器学习和地理信息系统的高质量数据产品;(3)用于数据管理和分析的容量基础设施;以及(4)自动环境数据收集。针对农业相关作物性状的加速育种是培育改良品种的关键,并且严重依赖于高分辨率、高通量的田间尺度表型分析技术,这些技术能够在更大的群体中以及跨多个环境有效地区分表现更好的品系。