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TSSC: a new deep learning model for accurate pea leaf disease identification.
Laixiang Xu1, Yibu Chang1, Chenyang Li1
1School of Computer and Data Science, Henan University of Urban Construction, Pingdingshan, China.
Frontiers in Plant Science
|December 17, 2025
Summary
This study introduces a deep learning model for accurate pea leaf disease recognition, achieving 99.61% accuracy. The intelligent system aids in early detection, improving crop yield and food safety.
Area of Science:
- Agricultural Science
- Computer Science
- Artificial Intelligence
Background:
- Accurate plant disease diagnosis is vital for crop yield and food safety.
- Automatic recognition of pea leaf diseases presents a significant challenge.
Purpose of the Study:
- To develop a deep learning-based intelligent recognition method for various pea leaf diseases.
- To address the automatic recognition problem of plant leaf diseases.
Main Methods:
- A novel deep learning framework, TSSC, was proposed.
- Incorporated a three-neighbor channel attention mechanism for enhanced feature extraction.
- Utilized a complementary squeeze and excitation mechanism and a split attention module to improve key feature extraction and reduce model complexity.
Main Results:
- The TSSC model achieved an outstanding overall classification accuracy of 99.61%.
- The proposed model demonstrated superior performance compared to other leading deep learning models.
Conclusions:
- The developed system offers an effective solution for the image recognition of complex plant diseases.
- The findings have valuable implications for the development of mobile disease detection equipment.
