College of Biosystems Engineering and Food Science, and State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310058, PR China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, PR China.
Section of Plant Breeding and Genetics, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA.
Trends Plant Sci. 2022 Feb;27(2):191-208. doi: 10.1016/j.tplants.2021.07.015. Epub 2021 Aug 17.
Optical sensors and sensing-based phenotyping techniques have become mainstream approaches in high-throughput phenotyping for improving trait selection and genetic gains in crops. We review recent progress and contemporary applications of optical sensing-based phenotyping (OSP) techniques in cereal crops and highlight optical sensing principles for spectral response and sensor specifications. Further, we group phenotypic traits determined by OSP into four categories - morphological, biochemical, physiological, and performance traits - and illustrate appropriate sensors for each extraction. In addition to the current status, we discuss the challenges of OSP and provide possible solutions. We propose that optical sensing-based traits need to be explored further, and that standardization of the language of phenotyping and worldwide collaboration between phenotyping researchers and other fields need to be established.
光学传感器和基于传感的表型分析技术已经成为高通量表型分析中的主流方法,可用于改进作物的性状选择和遗传增益。我们综述了基于光学传感的表型分析(OSP)技术在谷类作物中的最新进展和当代应用,并重点介绍了光谱响应和传感器规格的光学传感原理。此外,我们将 OSP 确定的表型性状分为四类——形态、生化、生理和性能性状,并举例说明了每种提取方法适用的传感器。除了当前的状况,我们还讨论了 OSP 的挑战并提供了可能的解决方案。我们提出需要进一步探索基于光学传感的性状,并且需要建立表型分析语言的标准化以及表型研究人员与其他领域之间的全球合作。