LANet: Enhancing Plant Disease Recognition Through Transfer Learning and Layer Attention

Accurate recognition of plant diseases plays a critical role in maintaining agricultural productivity and food security. This study proposes LANet, an innovative network architecture aimed at improving the precision of identifying and categorizing plant diseases from images. LANet consists of two primary components. Firstly, Layer Attention applies self-attention across feature layers at different scales to capture the weights between cross-scale features, addressing the data loss caused by downsampling and enhancing plant species recognition. Secondly, we introduce a transfer learning method, where a plant species classification network is initially trained, its parameters are then frozen, and several convolutional layers are added to train the disease classification network. This allows the model to leverage the learned classification information for more accurate disease recognition. Our model demonstrates an accuracy of 85.62%, outperforming nine other models significantly. Comparative analyses using the public PlantVillage dataset highlight the superior performance of our method in disease recognition.