Computer Engineering and Applications ›› 2023, Vol. 59 ›› Issue (6): 30-44.DOI: 10.3778/j.issn.1002-8331.2205-0604

• Research Hotspots and Reviews • Previous Articles     Next Articles

Survey of Deep Learning Algorithms for Agricultural Pest Detection

JIANG Xinlu, CHEN Tian’en, WANG Cong, LI Shuqin, ZHANG Hongming, ZHAO Chunjiang   

  1. 1.College of Information Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
    2.National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
    3.Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
  • Online:2023-03-15 Published:2023-03-15



  1. 1.西北农林科技大学 信息工程学院,陕西 杨凌 712100
    2.国家农业信息化工程技术研究中心,北京 100097
    3.北京市农林科学院 信息技术研究中心,北京 100097

Abstract: Pest detection is a key step in pest forecasting, which is of great significance to pest control, and is also a prerequisite for ensuring crop yield and quality. In recent years, with the rapid development of convolutional neural networks, pest detection technology has entered the era of intelligence, using deep learning related technologies to achieve accurate pest detection has become a research topic that researchers attach great attention to. To facilitate the development of pest detection techniques in deep learning, an overview of existing detection algorithms and datasets will be presented. The four difficult problems currently faced, such as data scarcity, small target detection, multi-scale detection, and dense and occlusion detection, are summarized and the main causes are analyzed. Focusing on the above difficult problems, the improvement strategies and technical details of the deep learning pest detection algorithms proposed in recent years are summarized, as well as the application algorithms for practical scenarios. The performance of various algorithms, the applicable scenarios of improvement strategies and their advantages and disadvantages are compared and analyzed. Finally, the potential development direction of pest detection is analyzed and prospected from the aspects of complex detection scenarios, lack of data, incremental update model and application landing.

Key words: deep learning, target detection, pest detection

摘要: 害虫检测是害虫测报的关键步骤,对于害虫防治具有重要意义,也是保证农作物产量和品质的前提。近年来,随着卷积神经网络的迅速发展,害虫检测技术进入智能化时代,使用深度学习相关技术实现精确的害虫检测已成为研究人员重点关注的课题。为了促进深度学习害虫检测技术的发展,对检测算法和现有数据集进行综述。总结了当前面临的数据匮乏、小目标检测、多尺度检测和密集与遮挡检测等四大难点问题,并分析了其主要成因。重点针对以上难点问题,总结归纳了近年来提出的深度学习害虫检测算法的改进策略和技术细节,以及面向实际场景的应用算法,对比分析了各类算法的性能表现、改进策略的适用场景及其优缺点。从面向复杂检测场景、解决数据匮乏问题、模型增量更新和应用落地等方面分析并展望了未来的研究趋势。

关键词: 深度学习, 目标检测, 害虫检测