Overview of Pest Detection and Recognition Algorithms

文献类型： 外文期刊

第一作者： Guo, Boyu

作者： Guo, Boyu;Wang, Jianji;Guo, Minghui;Chen, Miao;Chen, Yanan;Guo, Boyu;Wang, Jianji;Guo, Minghui;Chen, Miao;Chen, Yanan;Guo, Minghui;Miao, Yisheng

作者机构：

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.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序