Identification of rice plant diseases using lightweight attention networks

Rice is one of the most important crops in the world, and most people consume rice as a staple food, especially in Asian countries. Various rice plant diseases have a negative effect on crop yields. If proper detection is not taken, they can spread and lead to a significant decline in agricultural productions. In severe cases, they may even cause no grain harvest entirely, thus having a devastating impact on food security. The deep learning-based CNN methods have become the standard methods to address most of the technical challenges related to image identification and classification. In this study, to enhance the learning capability for minute lesion features, we chose the MobileNet-V2 pre-trained on ImageNet as the backbone network and added the attention mechanism to learn the importance of inter-channel relationship and spatial points for input features. In the meantime, the loss function was optimized and the transfer learning was performed twice for model training. The proposed procedure presents a superior performance relative to other state-of-the-art methods. It achieves an average identification accuracy of 99.67% on the public dataset. Even under complicated backdrop conditions, the average accuracy reaches 98.48% for identifying rice plant diseases. Experimental findings demonstrate the validity of the proposed procedure, and it is accomplished efficiently for rice disease identification.

Automated summary

Rice is a crucial crop, but diseases can harm yields and food security. This study utilizes deep learning and attention mechanisms to optimize model training, achieving efficient rice disease identification with high accuracy.