Abstract
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.
Highlights
Plant diseases and pests detection is a very important research content in the field of machine vision
The network structure is relatively complex, and a pixelby-pixel label is required when adding segmentation branches speaking, plant diseases and pests detection network based on deep learning can be divided into: two stage network represented by Faster R-convolutional neural network (CNN) [54]; one stage network represented by SSD [55] and YOLO [56–58]
Conclusions and future directions Compared with traditional image processing methods, which deal with plant diseases and pests detection tasks in several steps and links, plant diseases and pests detection methods based on deep learning unify them into end-to-end feature extraction, which has a broad development prospects and great potential
Summary
Plant diseases and pests detection is a very important research content in the field of machine vision. Machine vision-based plant diseases and pests detection equipment has been initially applied in agriculture and has replaced the traditional naked eye identification to some extent. For traditional machine vision-based plant diseases and pests detection method, conventional image processing algorithms or manual design of features plus classifiers. Liu and Wang Plant Methods (2021) 17:22 conditions. At this time, the traditional classical methods often appear helpless, and it is difficult to achieve better detection results. Plant diseases and pests detection method based on deep learning has important academic research value, and has a very broad market application prospect
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
