文献类型： 外文期刊
作者： Guo, Boyu 1 ; Wang, Jianji 1 ; Guo, Minghui 1 ; Chen, Miao 1 ; Chen, Yanan 1 ; Miao, Yisheng 4 ;
作者机构： 1.Xi An Jiao Tong Univ, Natl Engn Res Ctr Visual Informat & Applicat, Natl Key Lab Human Machine Hybrid Augmented Intell, Xian 710049, Peoples R China
2.Xi An Jiao Tong Univ, Inst Artificial Intelligence & Robot, Xian 710049, Peoples R China
3.Xi An Jiao Tong Univ, Sch Software Engn, Xian 710049, Peoples R China
4.Beijing Acad Agr & Forestry Sci, Res Ctr Informat Technol, Beijing 100097, Peoples R China
关键词： smart agriculture; pest detection; pest recognition
期刊名称：ELECTRONICS （ 影响因子：2.6； 五年影响因子：2.6 ）
ISSN：
年卷期： 2024 年 13 卷 15 期
页码：
收录情况： SCI
摘要： Detecting and recognizing pests are paramount for ensuring the healthy growth of crops, maintaining ecological balance, and enhancing food production. With the advancement of artificial intelligence technologies, traditional pest detection and recognition algorithms based on manually selected pest features have gradually been substituted by deep learning-based algorithms. In this review paper, we first introduce the primary neural network architectures and evaluation metrics in the field of pest detection and pest recognition. Subsequently, we summarize widely used public datasets for pest detection and recognition. Following this, we present various pest detection and recognition algorithms proposed in recent years, providing detailed descriptions of each algorithm and their respective performance metrics. Finally, we outline the challenges that current deep learning-based pest detection and recognition algorithms encounter and propose future research directions for related algorithms.
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