Research on agricultural disease recognition methods based on very large Kernel convolutional network-RepLKNet

Agricultural diseases pose significant challenges to plant production. With the rapid advancement of deep learning, the accuracy and efficiency of plant disease identification have substantially improved. However, conventional convolutional neural networks that rely on multi-layer small-kernel structures are limited in capturing long-range dependencies and global contextual information due to their constrained receptive fields. To overcome these limitations, this study proposes a plant disease recognition method based on RepLKNet, a convolutional architecture with large kernel designs that significantly expand the receptive field and enhance feature representation. Transfer learning is incorporated to further improve training efficiency and model performance. Experiments conducted on the Plant Diseases Training Dataset, comprising 95,865 images across 61 disease categories, demonstrate the effectiveness of the proposed method. Under five-fold cross-validation, the model achieved an overall accuracy (OA) of 96.03%, an average accuracy (AA) of 94.78%, and a Kappa coefficient of 95.86%. Compared with ResNet50 (OA: 95.62%) and GoogleNet (OA: 94.98%), the proposed model demonstrates competitive or superior performance. Ablation experiments reveal that replacing large kernels with 3×3 or 5×5 convolutions results in accuracy reductions of up to 1.1% in OA and 1.3% in AA, confirming the effectiveness of the large kernel design. These results demonstrate the robustness and superior capability of RepLKNet in plant disease recognition tasks.