Tomato Disease Recognition System Based on Image Automatic Labeling and Improved YOLO v5

ZHANG Lingxian, JING Jiaping, LI Shufei, ZHU Xinyi, QIAO Chen

Abstract

Intelligent recognition of crop diseases is a hot topic in the intersection of artificial intelligence and agriculture. At present, the crop disease identification system has a single function and lacks a system upgrade mechanism, and the cost of manual upgrade system is large. To solve the above problems, tomato disease was taken as an example, automatic tomato leaf image labeling algorithm was proposed based on OpenCV and an improved YOLO v5 tomato disease recognition model was constructed. Combining the ideas of automatic data set division, automatic model training and evaluation, and automatic creation and update of mobile phone APP were combined, and a tomato disease recognition system that can be automatically upgraded was designed. The expert review and correction mechanism was introduced to improve the reliability of the system identification results. The experimental results showed that the system realized the identification of the healthy leaves of tomato and the nine kinds of disease leaves, it can automatically expand the tomato disease image data set while identifying tomato diseases through the mobile phone APP in practical application, and automatically start the upgrade and optimization process of the system according to the number of data expansion, so as to continuously improve the tomato disease recognition performance of the system. The design of the system can provide a convenient and reliable tool for tomato disease identification in tomato production.


Keywords: tomato, neural network, automatic annotation, disease recognition, expert review, automatic upgrade

 

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