Design and Implementation of Peach Leaf Disease Identification System
School of Information Engineering, Henan Institute of Science and Technology, Xinxiang 453003, China
| Abstract: | Peach trees are widely cultivated in China. With the improvement of people's living standards, the requirements for the quality of peaches are constantly improving. The occurrence of peach leaf diseases has seriously affected the yield and quality of peach fruits. The production of high-quality peach fruit requires the necessary management of peach leaf diseases. The traditional identification method of diseased leaves is manual identification, which is subjective and difficult. This paper proposes a method of peach leaf disease detection using Anaconda+Python+Pycharm software and YOLOv5 model after adding attention mechanism CBAM, and develops a GUI interface recognition system for users to operate. The software design interface mainly includes login interface, registration interface and main detection interface. Fruit growers can import the collected peach leaf disease pictures into the recognition system, and then check the selection weight module of the main interface in this software to select the appropriate weight. Then initialize the model, and automatically compare it with the data set types of five leaf diseases including peach leaf shrinkage disease, bacterial perforation disease, yellow leaf disease, brown spot perforation disease and anthrax in this system through the picture detection module, so as to obtain the type of peach leaf disease, and provide corresponding prevention and control suggestions according to the type of leaf disease. The development of this system has improved the identification accuracy of peach leaf diseases by 2.15%. IOU increased by 3.02% to 82.37%. It reduces the misdiagnosis rate of fruit growers, increases the recognition accuracy of peach leaf disease, takes correct control measures, reduces the harm of leaf disease, and improves the income of fruit growers. |
| Keywords: | Peach Leaf Disease; Deep Learning; Interface Design; YOLOv5; Disease Detection |
| DOI: | 10.57237/j.cst.2023.01.001 |
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Received: 7 December 2022
Accepted: 16 January 2023
Published Online: 3 February 2023
Funding
| 1. | 河南省科技厅科技公关项目《基于机器学习的农作物病害识别与物理防控》 (222102210116) |
| 2. | 《基于AIoT和物理技术防控害虫的绿色生态果园》 (212102310553). |
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