Tayade Rupesh, Yoon Jungbeom, Lay Liny, Khan Abdul Latif, Yoon Youngnam, Kim Yoonha
Department of Applied Biosciences, Kyungpook National University, Daegu 41566, Korea.
Horticultural and Herbal Crop Environment Division, National Institute of Horticultural and Herbal Science, Rural Development Administration, Wanju 55365, Korea.
Plants (Basel). 2022 Jun 28;11(13):1712. doi: 10.3390/plants11131712.
The conventional plant breeding evaluation of large sets of plant phenotypes with precision and speed is very challenging. Thus, consistent, automated, multifaceted, and high-throughput phenotyping (HTP) technologies are becoming increasingly significant as tools to aid conventional breeding programs to develop genetically improved crops. With rapid technological advancement, various vegetation indices (VIs) have been developed. These VI-based imaging approaches, linked with artificial intelligence and a variety of remote sensing applications, provide high-throughput evaluations, particularly in the field of precision agriculture. VIs can be used to analyze and predict different quantitative and qualitative aspects of vegetation. Here, we provide an overview of the various VIs used in agricultural research, focusing on those that are often employed for crop or vegetation evaluation, because that has a linear relationship to crop output, which is frequently utilized in crop chlorophyll, health, moisture, and production predictions. In addition, the following aspects are here described: the importance of VIs in crop research and precision agriculture, their utilization in HTP, recent photogrammetry technology, mapping, and geographic information system software integrated with unmanned aerial vehicles and its key features. Finally, we discuss the challenges and future perspectives of HTP technologies and propose approaches for the development of new tools to assess plants' agronomic traits and data-driven HTP resolutions for precision breeding.
对大量植物表型进行精确且快速的传统植物育种评估极具挑战性。因此，作为辅助传统育种计划培育遗传改良作物的工具，一致、自动化、多方面且高通量的表型分析（HTP）技术正变得愈发重要。随着技术的快速进步，各种植被指数（VIs）得以开发。这些基于植被指数的成像方法与人工智能及多种遥感应用相结合，可提供高通量评估，尤其在精准农业领域。植被指数可用于分析和预测植被的不同定量和定性方面。在此，我们概述农业研究中使用的各种植被指数，重点关注那些常用于作物或植被评估的指数，因为它们与作物产量呈线性关系，常用于作物叶绿素、健康状况、水分及产量预测。此外，本文还描述了以下方面：植被指数在作物研究和精准农业中的重要性、它们在高通量表型分析中的应用、近期的摄影测量技术、测绘以及与无人机集成的地理信息系统软件及其关键特性。最后，我们讨论了高通量表型分析技术面临的挑战和未来展望，并提出开发评估植物农艺性状新工具的方法以及用于精准育种的数据驱动型高通量表型分析解决方案。
