A global–local momentum contrastive framework with critical feature calibration for field plant disease identification in precision agriculture

Plant diseases can substantially affect crop production and food supply stability, making accurate disease identification important for effective crop management. Although deep learning has been widely applied to plant disease recognition, model performance may degrade in complex field environments because of large intra-class variations, subtle inter-class differences, and background interference. To address these challenges, we propose GLC-Net, which integrates global–local momentum contrastive learning with critical feature calibration (CFC) for field plant disease identification. Specifically, a label-guided momentum contrastive learning mechanism is introduced to dynamically construct multiple positive pairs through a labeled queue, thereby promoting intra-class compactness and inter-class separability in the feature space. In addition, a global–local contrastive strategy based on cropped local views is designed to enhance multi-scale representation learning, enabling the model to capture both global contextual information and fine-grained pathological details. Furthermore, the proposed CFC module calibrates global representations using complementary local features, allowing the network to preserve more disease-relevant information under complex background conditions. Experiments on the Paddy and Soybean disease datasets demonstrate that GLC-Net consistently improves recognition performance across different backbone networks and compares favorably with representative existing methods. Ablation and visualization analyses further verify the contributions of the proposed components to feature discrimination and disease-region representation. These results demonstrate the effectiveness of GLC-Net for field plant disease identification, while further evaluation across additional crop species and more diverse field conditions remains necessary to establish its broader generalization capability.