To ensure food security in the face of increasing global demand due to population growth and progressive urbanization, it will be crucial to integrate emerging technologies in multiple disciplines to accelerate overall throughput of gene discovery and crop breeding. Plant agronomic traits often appear during the plants’ later growth stages due to the cumulative effects of their lifetime interactions with the environment. Therefore, decoding plant–environment interactions by elucidating plants’ temporal physiological responses to environmental changes throughout their lifespans will facilitate the identification of genetic and environmental factors, timing and pathways that influence complex end-point agronomic traits, such as yield. Here, we discuss the expected role of the life-course approach to monitoring plant and crop health status in improving crop productivity by enhancing the understanding of plant–environment interactions. We review recent advances in analytical technologies for monitoring health status in plants based on multi-omics analyses and strategies for integrating heterogeneous datasets from multiple omics areas to identify informative factors associated with traits of interest. In addition, we showcase emerging phenomics techniques that enable the noninvasive and continuous monitoring of plant growth by various means, including three-dimensional phenotyping, plant root phenotyping, implantable/injectable sensors and affordable phenotyping devices. Finally, we present an integrated review of analytical technologies and applications for monitoring plant growth, developed across disciplines, such as plant science, data science and sensors and Internet-of-things technologies, to improve plant productivity.

中文翻译：

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解码影响作物农艺性状的植物与环境的相互作用。

在面对人口增长和城市化进程带来的全球需求增长的情况下，要确保食品安全，至关重要的是将新兴技术整合到多个学科中，以加快基因发现和作物育种的整体吞吐量。由于植物一生与环境相互作用的累积效应，植物农艺性状通常在植物的后期生长阶段出现。因此，通过阐明植物在整个生命周期中对环境变化的时间生理反应来解码植物与环境之间的相互作用，将有助于识别影响复杂终点农艺性状（如产量）的遗传和环境因素，时机和途径。这里，我们将讨论生命过程方法对植物和作物健康状况的监控在通过增进对植物与环境相互作用的理解来提高作物生产力方面的预期作用。我们回顾了基于多组学分析和用于整合来自多个组学领域的异构数据集以识别与关注性状相关的信息因素的策略的，用于监测植物健康状况的分析技术的最新进展。此外，我们展示了新兴的表观技术，可通过多种方式对植物生长进行无创且连续的监测，包括三维表型，植物根表型，可植入/可注射传感器和价格合理的表型设备。最后，我们对用于监测植物生长的分析技术和应用进行了综合综述，