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Classification and detection of insects from field images using deep learning for smart pest management: A systematic review

文献类型： 外文期刊

作者： Li, Wenyong 1 ; Zheng, Tengfei 1 ; Yang, Zhankui 1 ; Li, Ming 1 ; Sun, Chuanheng 1 ; Yang, Xinting 1 ;

作者机构： 1.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

3.Natl Engn Lab Agriprod Qual Traceabil, Beijing 100097, Peoples R China

4.Shanghai Ocean Univ, Coll Informat, Shanghai 201306, Peoples R China

5.Beijing Univ Technol, Coll Comp Sci & Technol, Beijing 100124, Peoples R China

关键词： Deep learning; Classification and detection; Smart pest monitoring; Computer vision; Plant protection

期刊名称：ECOLOGICAL INFORMATICS （ 影响因子：3.142； 五年影响因子：3.029 ）

ISSN： 1574-9541

年卷期： 2021 年 66 卷

页码：

收录情况： SCI

摘要： Insect pest is one of the main causes affecting agricultural crop yield and quality all over the world. Rapid and reliable insect pest monitoring plays a crucial role in population prediction and control actions. The great breakthrough of deep learning (DL) technology has resulted in its successful applications in various fields, including automatic insect pest monitoring. DL creates both new strengths and a series of challenges for data processing in smart pest monitoring (SPM). This review outlines the technical methods of DL frameworks and their applications in SPM with emphasis on insect pest classification and detection using field images. The methodologies and technical details evolved in insect pest classification and detection using DL are summarized and distilled during different processing stages: image acquisition, data preprocessing and modeling techniques. Finally, a general framework is provided to facilitate the smart insect monitoring and future challenges and trends are highlighted. In a word, our purpose is to provide researchers and technicians with a better understanding of DL techniques and their state-of-art achievements in SPM, which can promote the implement of various SPM applications.

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