Progress in the Application of CNN-Based Image Classification and Recognition in Whole Crop Growth Cycles

文献类型： 外文期刊

第一作者： Yu, Feng

作者： Yu, Feng;Xiao, Jun;Yu, Feng;Zhang, Qian;Ma, Yuntao;Wang, Ming;Luan, Rupeng;Liu, Xin;Ping, Yang;Nie, Ying;Tao, Zhenyu;Zhang, Hui

作者机构：

关键词： convolution neural network; crop classification; crop recognition; whole crop growth cycle

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

ISSN：

年卷期： 2023 年 15 卷 12 期

页码：

收录情况： SCI

摘要： The categorization and identification of agricultural imagery constitute the fundamental requisites of contemporary farming practices. Among the various methods employed for image classification and recognition, the convolutional neural network (CNN) stands out as the most extensively utilized and swiftly advancing machine learning technique. Its immense potential for advancing precision agriculture cannot be understated. By comprehensively reviewing the progress made in CNN applications throughout the entire crop growth cycle, this study aims to provide an updated account of these endeavors spanning the years 2020 to 2023. During the seed stage, classification networks are employed to effectively categorize and screen seeds. In the vegetative stage, image classification and recognition play a prominent role, with a diverse range of CNN models being applied, each with its own specific focus. In the reproductive stage, CNN's application primarily centers around target detection for mechanized harvesting purposes. As for the post-harvest stage, CNN assumes a pivotal role in the screening and grading of harvested products. Ultimately, through a comprehensive analysis of the prevailing research landscape, this study presents the characteristics and trends of current investigations, while outlining the future developmental trajectory of CNN in crop identification and classification.

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