TSSC: A New Deep Learning Model for Accurate Pea Leaf Disease Identification

Laixiang Xu¹Yibu Chang¹Yang Zhang²Xiaodong Yang³Xinjia Chen¹Zhaopeng Cai¹Junmin Zhao^1*

• ¹Henan University of Urban Construction, Pingdingshan, China
• ²Chinese academy of science, Beijing, China
• ³Chinese Academy of Agricultural Sciences, Beijing, China

Problem: Accurate diagnosis of plant diseases is crucial for ensuring crop yield and food safety. This study aims to explore a deep learning based intelligent recognition methods for plant leaf diseases to solve the automatic recognition problem of various pea leaf diseases. Methodology: We propose a novel deep learning framework called TSSC. First, a three-neighbor channel attention is designed to promote the effectiveness of feature extraction. Second, a complementary squeeze and excitation mechanism is introduced to enhance the ability to extract key features. Finally, a split attention module is embedded to reduce model complexity. Results: The experimental results demonstrate that the proposed model achieves an overall classification accuracy of 99.61% and outperforms other excellent deep learning models. Contribution: The currently proposed system provides an effective solution for image recognition of complex plant diseases and has reference value for the development of mobile disease detection equipment.