High-Precision Crop Leaf Disease Identification Using MMCA-ConvNeXt with Multi-scale Multidimensional Collaborative Attention

Accurate identification of crop diseases is crucial for enhancing agricultural yields. In the agricultural field, crop disease images vary in resolution and aspect ratio due to different imaging equipment, distances, and environmental conditions, which can affect the accuracy and consistency of disease detection models. We propose a new model, MMCA-ConvNeXt, based on ConvNeXt-T. This model incorporates our novel Multi-scale Multidimensional Collaborative Attention (MMCA) mechanism, which enhances the receptive field of the original model while capturing features at multiple resolutions. This enables better generalization across different crop disease datasets, improving performance and robustness. Additionally, we replaced layer normalization in the downsampling layer and post-global average pooling with Batch Channel Normalization, which allows better exploitation of inter-channel information and enhances gradient flow within the model. Experiments using publicly available datasets for rice, maize, and the 2018 AI Challenger demonstrate the superior performance of our ConvNeXt-based model, achieving accuracies of 99.53% for rice, 99.93% for maize, and 87.57% for the 2018 AI Challenger dataset. It outperforms VGG - 19, ResNet - 101, EfficientNet, and several transformer models with fewer parameters. MMCA shows the best results among different attention mechanisms for plant leaf disease detection, and ablation studies confirm the model’s performance enhancements.