High-throughput phenotyping of plant leaf morphological, physiological,and biochemical traits on multiple scales using optical sensing

作     者：Huichun Zhang Lu Wang Xiuliang Jin Liming Bian Yufeng Ge Huichun Zhang;Lu Wang;Xiuliang Jin;Liming Bian;Yufeng Ge

作者机构：College of Mechanical and Electronic EngineeringNanjing Forestry UniversityNanjing 210037JiangsuChina Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest ResourcesNanjing Forestry UniversityNanjing 210037JiangsuChina Institute of Crop SciencesChinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and EcologyMinistry of AgricultureBeijing 100081China College of ForestryNanjing Forestry UniversityNanjing 210037JiangsuChina Co-Innovation Center for Sustainable Forestry in Southern China and Key Laboratory of Forest Genetics&Biotechnology of the Ministry of EducationNanjing Forestry UniversityNanjing 210037JiangsuChina Department of Biological Systems EngineeringUniversity of Nebraska-LincolnNE 68583USA 

出 版 物：《作物学报:英文版》 (The Crop Journal)

年 卷 期：2023年第11卷第5期

页      面：1303-1318页

核心收录：

学科分类：0710[理学-生物学] 07[理学] 080202[工学-机械电子工程] 08[工学] 0901[农学-作物学] 0802[工学-机械工程] 0713[理学-生态学] 

基　　金：supported by the National Natural Science Foundation of China (32171790 and 32171818) Jiangsu Province Modern Agricultural Machinery Equipment and Technology Demonstration Promotion Project (NJ2020-18) Key Research and Development Program of Jiangsu Province (BE2021307) Qinglan Project Foundation of Jiangsu province 333 Project of Jiangsu Province 

主　　题：Leaf traits Optical sensing Image processing Machine learning Artificial intelligence 

摘      要：Acquisition of plant phenotypic information facilitates plant breeding, sheds light on gene action, and can be applied to optimize the quality of agricultural and forestry products. Because leaves often show the fastest responses to external environmental stimuli, leaf phenotypic traits are indicators of plant growth,health, and stress levels. Combination of new imaging sensors, image processing, and data analytics permits measurement over the full life span of plants at high temporal resolution and at several organizational levels from organs to individual plants to field populations of plants. We review the optical sensors and associated data analytics used for measuring morphological, physiological, and biochemical traits of plant leaves on multiple scales. We summarize the characteristics, advantages and limitations of optical sensing and data-processing methods applied in various plant phenotyping scenarios. Finally, we discuss the future prospects of plant leaf phenotyping research. This review aims to help researchers choose appropriate optical sensors and data processing methods to acquire plant leaf phenotypes rapidly,accurately, and cost-effectively.