Bridging CNNs and vision transformers for efficient tea leaf phytopathogen diagnosis: GL-MobFormer.

Background: Accurate identification of visible disease symptoms is essential for the sustainable management of tea ( Methods: We propose GL-MobFormer, a lightweight hybrid deep learning framework. This architecture integrates the local feature extraction capabilities of MobileNetV3 with the global contextual modeling of a Transformer Encoder. To improve model robustness in unstructured field environments, we applied the CutMix data augmentation strategy. The framework was evaluated on a dataset comprising 5,278 tea leaf images across seven phytosanitary categories. Results: Empirical evaluations demonstrate that GL-MobFormer achieved a classification accuracy of 95.13% and a Matthews Correlation Coefficient (MCC) of 0.9417. Crucially, this performance was maintained with a low computational footprint of merely 0.33 G FLOPs(Floating Point Operations). Importantly, an occlusion-based sensitivity protocol was implemented to provide quantitative grounding for model interpretability. Results revealed that systematically masking only the top 5% of critical activation regions led to an average reduction of 70.61% in classification confidence, empirically confirming that the model's diagnostic logic is faithfully anchored on pathologically relevant lesion features rather than background noise. Conclusion: GL-MobFormer achieves an optimal trade-off between diagnostic precision and computational overhead. It provides a practical and highly efficient solution for on-site, real-time phytosanitary monitoring in precision agriculture.