DAPR-AM-Net: an end-to-end smart farming system powered by dual-attention progressive refinement and adaptive MixUp for explainable tomato leaf disease classification and forecasting.

Vision-based crop disease diagnosis plays a pivotal role in smart agriculture, yet challenges such as complex field backgrounds, high intra-class similarity of lesion morphology, and severe data imbalance continue to impede model stability and interpretability. To address these issues, this study proposes DAPR-AM-Net, an intelligent diagnostic framework for tomato leaf diseases that integrates dual-attention progressive refinement with adaptive MixUp. The method introduces four key innovations: (1) a Dual Attention Fusion Mechanism (DAFM) that jointly leverages channel-wise and spatial attention to enhance lesion-related texture, color, and structural cues while suppressing background noise via the CBAM module, thereby directing the network's focus toward pathogenic regions; (2) an Adaptive MixUp with Attention-Aware Sampling (AMAAS) module that dynamically adjusts sample mixing ratios according to attention maps, effectively improving discrimination in complex boundary areas; (3) a Progressive Feature Refinement with Dual Attention (PFR-DA) module that incrementally optimizes deep feature representations through cross-hierarchical information flows; and (4) an Imbalance-Aware Multi-Objective Optimization (IAMOO) strategy that adaptively modulates loss weights based on category distribution to strengthen recognition of minority disease classes. On our self-constructed Tomato-DD dataset, DAPR-AM-Net achieves superior performance across all major metrics-including an accuracy of 99.73%, precision of 99.73%, recall of 99.74%, and an F1-score of 99.73%-outperforming current state-of-the-art approaches. On the full Plant-Village dataset, the model achieves 99.85% accuracy, 99.78% precision, 99.84% recall, and a 99.81% F1-score, while maintaining a compact model size of only 4.72 M parameters. Multi-level interpretability analyses corroborate the transparency and reliability of the model's inference process. Additionally, we developed an end-to-end smart agriculture platform powered by DAPR-AM-Net. Overall, DAPR-AM-Net provides a forward-looking yet practical solution for high-accuracy and strongly interpretable disease diagnosis in smart agriculture scenarios, demonstrating both methodological innovation and real-world applicability.