Advancements in Utilizing Image-Analysis Technology for Crop-Yield Estimation

文献类型： 外文期刊

第一作者： Yu, Feng

作者： Yu, Feng;Xiao, Jun;Yu, Feng;Wang, Ming;Zhang, Qian;Zhang, Jinmeng;Liu, Xin;Ping, Yang;Luan, Rupeng

作者机构： Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China;Beijing Acad Agr & Forestry Sci, Inst Data Sci & Agr Econ, Beijing 100097, Peoples R China

关键词： remote sensing images; visible light images; machine learning; deep learning; biomass; yield calculation

期刊名称：REMOTE SENSING （ 2022影响因子：5.0； 五年影响因子：5.6 ）

ISSN：

年卷期： 2024 年 16 卷 6 期

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收录情况： SCI

摘要： Yield calculation is an important link in modern precision agriculture that is an effective means to improve breeding efficiency and to adjust planting and marketing plans. With the continuous progress of artificial intelligence and sensing technology, yield-calculation schemes based on image-processing technology have many advantages such as high accuracy, low cost, and non-destructive calculation, and they have been favored by a large number of researchers. This article reviews the research progress of crop-yield calculation based on remote sensing images and visible light images, describes the technical characteristics and applicable objects of different schemes, and focuses on detailed explanations of data acquisition, independent variable screening, algorithm selection, and optimization. Common issues are also discussed and summarized. Finally, solutions are proposed for the main problems that have arisen so far, and future research directions are predicted, with the aim of achieving more progress and wider popularization of yield-calculation solutions based on image technology.

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