Wheat disease recognition method based on the SC-ConvNeXt network model

When utilizing convolutional neural networks for wheat disease identification, the training phase typically requires a substantial amount of labeled data. However, labeling data is both complex and costly. Additionally, the model's recognition performance is often disrupted by complex factors in natural environments. To address these issues, this paper proposes a wheat disease identification model, SC-ConvNeXt, which integrates the SimCLR pre-training framework and an improved CBAM attention mechanism. The model employs ConvNeXt-T as the feature extraction network. Initially, it uses the self-supervised SimCLR pre-training framework to learn inter-class similarities, reducing the reliance on labeled data during training. Subsequently, the CBAM attention module is integrated into ConvNeXt-T to enhance the model's feature extraction and generalization capabilities in complex backgrounds, and each attention module's loss function is improved with a LeakyReLU activation function to prevent neuron deactivation when inputs are negative. Furthermore, by introducing the Focal Loss function, the model addresses the imbalance in the quantity of easy and difficult-to-classify samples. The dataset used in this study comes from the 'Smart Agriculture' platform of Jilin Agricultural Science and Technology College, including images of three wheat diseases and healthy wheat. After expanding the dataset with various data augmentation methods, the effectiveness of adding SimCLR and the attention mechanism was sequentially verified. Comparative experiments were also conducted against four classic classification models. The experimental results show that the proposed SC-ConvNeXt model achieves an average classification accuracy of 88.05% on the test set, the highest among all comparative models. The model does not require additional labeled data during training, demonstrating its effectiveness in enhancing wheat disease recognition performance under natural environmental conditions without the need for extra labeled data training.