Li Daoliang, Quan Chaoqun, Song Zhaoyang, Li Xiang, Yu Guanghui, Li Cheng, Muhammad Akhter
National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China.
Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, China Agricultural University, Beijing, China.
Front Bioeng Biotechnol. 2021 Jan 13;8:623705. doi: 10.3389/fbioe.2020.623705. eCollection 2020.
Food scarcity, population growth, and global climate change have propelled crop yield growth driven by high-throughput phenotyping into the era of big data. However, access to large-scale phenotypic data has now become a critical barrier that phenomics urgently must overcome. Fortunately, the high-throughput plant phenotyping platform (HT3P), employing advanced sensors and data collection systems, can take full advantage of non-destructive and high-throughput methods to monitor, quantify, and evaluate specific phenotypes for large-scale agricultural experiments, and it can effectively perform phenotypic tasks that traditional phenotyping could not do. In this way, HT3Ps are novel and powerful tools, for which various commercial, customized, and even self-developed ones have been recently introduced in rising numbers. Here, we review these HT3Ps in nearly 7 years from greenhouses and growth chambers to the field, and from ground-based proximal phenotyping to aerial large-scale remote sensing. Platform configurations, novelties, operating modes, current developments, as well the strengths and weaknesses of diverse types of HT3Ps are thoroughly and clearly described. Then, miscellaneous combinations of HT3Ps for comparative validation and comprehensive analysis are systematically present, for the first time. Finally, we consider current phenotypic challenges and provide fresh perspectives on future development trends of HT3Ps. This review aims to provide ideas, thoughts, and insights for the optimal selection, exploitation, and utilization of HT3Ps, and thereby pave the way to break through current phenotyping bottlenecks in botany.
食物短缺、人口增长和全球气候变化推动了由高通量表型分析驱动的作物产量增长进入大数据时代。然而，获取大规模表型数据现已成为表型组学迫切需要克服的关键障碍。幸运的是，高通量植物表型分析平台（HT3P）采用先进的传感器和数据收集系统，能够充分利用无损和高通量方法来监测、量化和评估大规模农业实验中的特定表型，并且能够有效执行传统表型分析无法完成的表型任务。通过这种方式，HT3P是新颖且强大的工具，最近各种商业、定制甚至自主研发的HT3P数量不断增加。在此，我们回顾近7年来从温室、生长室到田间，从地面近端表型分析到空中大规模遥感的这些HT3P。全面且清晰地描述了平台配置、新颖之处、运行模式、当前发展情况以及不同类型HT3P的优缺点。然后，首次系统地展示了用于比较验证和综合分析的HT3P的各种组合。最后，我们考虑当前的表型挑战，并为HT3P的未来发展趋势提供新的视角。本综述旨在为HT3P的最佳选择、开发和利用提供思路、想法和见解，从而为突破当前植物学表型分析瓶颈铺平道路。
