Huichun Zhang , Lu Wang , Xiuliang Jin , Liming Bian , Yufeng Ge
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引用次数: 2
Abstract
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.
Crop JournalAgricultural and Biological Sciences-Agronomy and Crop Science
CiteScore
9.90
自引率
3.00%
发文量
638
审稿时长
41 days
期刊介绍:
The major aims of The Crop Journal are to report recent progresses in crop sciences including crop genetics, breeding, agronomy, crop physiology, germplasm resources, grain chemistry, grain storage and processing, crop management practices, crop biotechnology, and biomathematics.
The regular columns of the journal are Original Research Articles, Reviews, and Research Notes. The strict peer-review procedure will guarantee the academic level and raise the reputation of the journal. The readership of the journal is for crop science researchers, students of agricultural colleges and universities, and persons with similar academic levels.