Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China.
Center of Excellence for Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai 200032, China.
J Genet Genomics. 2024 Aug;51(8):790-800. doi: 10.1016/j.jgg.2024.04.016. Epub 2024 May 10.
Crop phenomics enables the collection of diverse plant traits for a large number of samples along different time scales, representing a greater data collection throughput compared with traditional measurements. Most modern crop phenomics use different sensors to collect reflective, emitted, and fluorescence signals, etc., from plant organs at different spatial and temporal resolutions. Such multi-modal, high-dimensional data not only accelerates basic research on crop physiology, genetics, and whole plant systems modeling, but also supports the optimization of field agronomic practices, internal environments of plant factories, and ultimately crop breeding. Major challenges and opportunities facing the current crop phenomics research community include developing community consensus or standards for data collection, management, sharing, and processing, developing capabilities to measure physiological parameters, and enabling farmers and breeders to effectively use phenomics in the field to directly support agricultural production.
作物表型组学能够在不同的时间尺度上收集大量样本的多种植物特性,与传统测量相比,代表了更高的数据采集通量。大多数现代作物表型组学使用不同的传感器,以不同的时空分辨率从植物器官中收集反射、发射和荧光等信号。这种多模态、高维数据不仅加速了作物生理学、遗传学和整个植物系统建模的基础研究,而且还支持优化田间农艺实践、植物工厂内部环境,最终支持作物育种。当前作物表型组学研究界面临的主要挑战和机遇包括:为数据收集、管理、共享和处理制定社区共识或标准,开发测量生理参数的能力,以及使农民和培育者能够在田间有效地使用表型组学,直接支持农业生产。