Design and Implementation of Crop Leaf Disease Detection System Based on Deep Learning

DOI: https://doi.org/10.62517/jlsa.202607105

Author(s)

Ran Tao, Qianqian Li*

Affiliation(s)

School of Artificial Intelligence and Software, Kewen College, Jiangsu Normal University, Xuzhou, Jiangsu, China *Corresponding author

Abstract

The precise and rapid detection of crop leaf diseases is a critical component in ensuring stable agricultural production and income growth, as well as advancing the development of smart agriculture. Traditional disease detection methods rely on manual observation and laboratory analysis, which suffer from low efficiency, subjective influence on identification results, and difficulties in adapting to large-scale field operations. Deep learning-based object detection algorithms, with their advantages of automated feature extraction and high recognition accuracy, provide a novel solution for crop leaf disease detection. This paper focuses on the YOLOv8 algorithm to construct a crop leaf disease recognition model, designing and implementing a disease detection system that integrates user management, multi-format image recognition, and flexible model switching. The system adheres to modular and hierarchical architectural design principles, utilizes the PyQt framework to develop a user-friendly interface, and employs SQLite database for efficient storage and management of user information and detection records. Tests demonstrate that the system features simple operation, rapid response, and accurate identification results, effectively enhancing the efficiency of crop leaf disease detection. It provides intelligent technical support for disease prevention and control in agricultural production, demonstrating strong practical application value.

Keywords

Deep Learning; YOLOv8; Crop Leaves; Disease Detection; Intelligent Recognition System; PyQt

References

[1]Chen Dayun. Analysis of Causes and Comprehensive Prevention of Common Crop Pests and Diseases. Farmer's Friend to Wealth, 2017, (08): 50 + 31. [2]Li Dandan, Liu Xinxin, Luo Yuanyi, et al. Research Progress and Challenges of Grain Storage Pest Image Target Detection Based on Deep Learning. Science and Technology of Cereals, Oils and Foods, 1-13. [3]Pan Tianyu, Yang Chen, Tang Yanhong, et al. Research on Improved YOLOv8 Rice Disease Recognition Model. Modern Agricultural Equipment, 2026, 47(01): 91-95. [4]Zhou Jianglong, Wang Tianyi, Li Lun, et al. Lightweight Crop Leaf Disease Recognition Model Based on Deep Learning. Jiangsu Agricultural Sciences, 2024, 52 (16): 230-238. [5]Deng Yue, Xiao Yang. International Practice of Smart Agriculture and the Development of China's Agricultural Modernization. Agricultural Economy and Management, 1-9 [6]Xu Mengxia, Xu Hao, Zheng Tao. In-depth Integration of UI Design Based on User Experience and Front-end and Back-end Function Implementation. Modern Transmission, 2025, (06): 50-54. [7]Fang Yingying, Pan Yanhong, Pan Xinling, et al. Design of Crop Pest and Disease Recognition System Based on Deep Learning. Journal of Smart Agriculture, 2026, 6(03): 23-28. [8]Xu Hongmei. Analysis of SQlite Application in Python. Neijiang Science and Technology, 2023, 44(04): 60-61. [9]Dong Qin, Yang Guoyu. Crop Pest and Disease Recognition Based on Optimized CBAM Improved YOLOv5. Computer Systems & Applications, 2023, 32(07): 261-268. [10]Tao Zhi, Kong Jianlei, Jin Xuebo, et al. Design of Crop Pest and Disease Image Recognition APP System Based on Deep Learning. Computer Applications and Software, 2022, 39(03): 341-345.