Liu Jun, Wang Xuewei
Shandong Provincial University Laboratory for Protected Horticulture, Blockchain Laboratory of Agricultural Vegetables, Weifang University of Science and Technology, Weifang, 262700, Shandong, China.
Plant Methods. 2021 Feb 24;17(1):22. doi: 10.1186/s13007-021-00722-9.
Plant diseases and pests are important factors determining the yield and quality of plants. Plant diseases and pests identification can be carried out by means of digital image processing. In recent years, deep learning has made breakthroughs in the field of digital image processing, far superior to traditional methods. How to use deep learning technology to study plant diseases and pests identification has become a research issue of great concern to researchers. This review provides a definition of plant diseases and pests detection problem, puts forward a comparison with traditional plant diseases and pests detection methods. According to the difference of network structure, this study outlines the research on plant diseases and pests detection based on deep learning in recent years from three aspects of classification network, detection network and segmentation network, and the advantages and disadvantages of each method are summarized. Common datasets are introduced, and the performance of existing studies is compared. On this basis, this study discusses possible challenges in practical applications of plant diseases and pests detection based on deep learning. In addition, possible solutions and research ideas are proposed for the challenges, and several suggestions are given. Finally, this study gives the analysis and prospect of the future trend of plant diseases and pests detection based on deep learning.
植物病虫害是决定植物产量和质量的重要因素。植物病虫害识别可通过数字图像处理手段进行。近年来，深度学习在数字图像处理领域取得突破，远优于传统方法。如何利用深度学习技术研究植物病虫害识别已成为研究人员高度关注的研究课题。本文综述给出了植物病虫害检测问题的定义，提出了与传统植物病虫害检测方法的比较。根据网络结构的差异，本研究从分类网络、检测网络和分割网络三个方面概述了近年来基于深度学习的植物病虫害检测研究，并总结了每种方法的优缺点。介绍了常用数据集，并比较了现有研究的性能。在此基础上，本研究探讨了基于深度学习的植物病虫害检测在实际应用中可能面临的挑战。此外，针对这些挑战提出了可能的解决方案和研究思路，并给出了几点建议。最后，本研究对基于深度学习的植物病虫害检测未来趋势进行了分析和展望。
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