TSSC: un nuevo modelo de aprendizaje profundo para la identificación precisa de enfermedades en hojas de guisante
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Laixiang Xu1, Yibu Chang1, Chenyang Li1
1School of Computer and Data Science, Henan University of Urban Construction, Pingdingshan, 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.
