Abstract：
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.
摘要：
作物表型组学能够在不同的时间尺度上收集大量样品的不同植物性状，表示与传统测量相比更大的数据收集吞吐量。大多数现代作物表型组学使用不同的传感器来收集反射，发射和荧光信号等。,来自不同时空分辨率的植物器官。这样的多模式，高维数据不仅加速了作物生理学的基础研究，遗传学,和整个工厂系统建模，而且还支持田间农艺实践的优化，工厂的内部环境，以及最终的作物育种。当前作物表型组学研究界面临的主要挑战和机遇包括发展社区共识或数据收集标准，管理,分享,和加工，发展测量生理参数的能力，并使农民和育种者能够有效地在田间使用表型组学直接支持农业生产。
