Identification of rice leaf disease based on DepMulti-Net.

Abstract

This research presents DepMulti-Net, a novel rice disease and pest identification model, designed to overcome the challenges of complex background interference, difficult disease feature extraction, and large model parameter volume in rice leaf disease identification. Initially, a comprehensive rice disease dataset comprising 20,000 images was meticulously constructed, covering four common types of rice diseases: bacterial leaf blight, rice blast, brown spot, and tungro disease. To enhance data diversity, various data augmentation techniques were applied. Subsequently, a novel VGG-block module was introduced. By leveraging depth-separable convolution, the model's parameter quantity was significantly reduced. A multi-scale feature fusion module was also designed to effectively enhance the model's ability to extract disease features from complex backgrounds. Moreover, the integration of the feature reuse mechanism and inverse bottleneck structure further improved the model's recognition accuracy for fine-grained disease features. Experimental results show that the DepMulti-Net model has only 13.50M parameters and achieves an average accuracy of 98.56% in identifying the four types of rice diseases. This performance significantly outperforms existing rice leaf disease identification methods. In conclusion, this study offers an efficient and lightweight solution for crop disease identification, which holds great significance for promoting the development of smart agriculture.

Code and data availability

The paper's Appendix A explicitly states that the self-built rice leaf disease dataset (20,000 field images) and the authors' analysis code for DepMulti-Net are publicly available at the authors' GitHub URL, which matches an allowed URL.