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Position: Home > Articles > High-throughput phenotyping of plant leaf morphological, physiological, and biochemical traits on multiple scales using optical sensing The Crop Journal 2023,11 (5)

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 G
单  位：
Institute of Crop Sciences, Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and Ecology, Ministry of Agriculture, Beijing 100081, China;Co-Innovation Center for Sustainable Forestry in Southern China and Key Laboratory of Forest Genetics & Biotechnology of the Ministry of Education, Nanjing Forestry University, Nanjing 210037, Jiangsu, China;Department of Biological Systems Engineering, University of Nebraska-Lincoln, NE 68583, US;Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, Jiangsu, China;College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, Jiangsu, China
关键词：
Artificial intelligence;Image processing;Leaf traits;Machine learning;Optical sensin
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.(c) 2023 Crop Science Society of China and Institute of Crop Science, CAAS. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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