Citrus Disease Classification Model Based on Improved ConvNeXt

Abstract

Early diagnosis of citrus diseases directly affects the yield and quality of citrus cultivation, and a citrus disease classification model based on improved ConvNeXt is proposed to address the problems of high cost and low efficiency of traditional citrus disease detection methods. Firstly, the attention mechanism is changed to a parallel connection in the convolutional block attention module, and the attention mechanism is incorporated into ConvNeXt, which improves the model's ability of feature extraction, makes the model focus more on lesion features, and suppresses the interference of background information. Secondly, the multi-scale feature fusion module is incorporated to improve the model's adaptability to disease features at different scales and improve the network classification performance. Finally, the transfer learning method is used to conduct pre-training with ImageNet weight information to reduce the impact of insufficient samples. The experimental results show that on the self-constructed complex background citrus disease dataset, the model in this paper achieves an average accuracy of 98.07%, which is improved by 2.9% compared with the original model, and the comprehensive performance is significantly better than that of VGG, AlexNet, and other models. The model in this paper improves the performance of disease classification and provides a theoretical basis for intelligent classification of citrus diseases.