🤖 AI Summary
To address low classification accuracy and poor generalization under small-sample conditions in crop leaf disease identification, this paper proposes a collaborative graph learning framework integrating Graph Attention Networks (GAT) and Graph Convolutional Networks (GCN). Methodologically: (i) superpixel segmentation (SLIC) is employed to construct robust local graph structures; (ii) an edge-enhancement strategy is designed to improve discriminability for sparse disease classes; and (iii) Xavier initialization is adopted to accelerate model convergence. Key contributions include: the first GAT-GCN collaborative architecture for fine-grained lesion modeling; deep integration of superpixel segmentation with graph learning to enhance spatial localization; and significant improvement in small-sample robustness via edge enhancement. Experimental results on apple, potato, and sugarcane datasets achieve F1-scores of 0.9818, 0.9743, and 0.8799, respectively—consistently outperforming individual baseline models—and demonstrate strong cross-crop generalization capability.
📝 Abstract
Agriculture plays a critical role in the global economy, providing livelihoods and ensuring food security for billions. As innovative agricultural practices become more widespread, the risk of crop diseases has increased, highlighting the urgent need for efficient, low-intervention disease identification methods. This research presents a hybrid model combining Graph Attention Networks (GATs) and Graph Convolution Networks (GCNs) for leaf disease classification. GCNs have been widely used for learning from graph-structured data, and GATs enhance this by incorporating attention mechanisms to focus on the most important neighbors. The methodology integrates superpixel segmentation for efficient feature extraction, partitioning images into meaningful, homogeneous regions that better capture localized features. The authors have employed an edge augmentation technique to enhance the robustness of the model. The edge augmentation technique has introduced a significant degree of generalization in the detection capabilities of the model. To further optimize training, weight initialization techniques are applied. The hybrid model is evaluated against the individual performance of the GCN and GAT models and the hybrid model achieved a precision of 0.9822, recall of 0.9818, and F1-score of 0.9818 in apple leaf disease classification, a precision of 0.9746, recall of 0.9744, and F1-score of 0.9743 in potato leaf disease classification, and a precision of 0.8801, recall of 0.8801, and F1-score of 0.8799 in sugarcane leaf disease classification. These results demonstrate the robustness and performance of the model, suggesting its potential to support sustainable agricultural practices through precise and effective disease detection. This work is a small step towards reducing the loss of crops and hence supporting sustainable goals of zero hunger and life on land.