Existing deep learning techniques for tomato leaf disease recognition face several challenges, including external environmental interference, limited dataset size, imbalanced sample distribution, and overlapping characteristics among different diseases, which complicate accurate disease identification. Furthermore, complex models with a high number of parameters are often difficult to deploy on resource-constrained embedded devices. To address these challenges, this paper proposes a novel tomato leaf disease recognition method based on DGP-SNNet. Initially, to mitigate issues related to imbalanced samples and overfitting, we introduce a two-stage transfer learning technique alongside a partial convolution module (PConv) to decrease data dependency and enhance model stability. Subsequently, we propose a Global Grouped Location Attention (GGLA) mechanism that dynamically adapts to capture fine-grained disease information, thereby addressing the similarities between disease categories. Finally, we employ a joint compression method utilizing Network Slimming and Neuron Selectivity Transfer, which significantly reduces model size with minimal loss in accuracy. Experimental results demonstrate a classification accuracy of 99.55%, with FLOPs of 1011.88 MB and a parameter count of 4.93 MB. Compared to the baseline model, accuracy improved by 2.23%, FLOPs decreased by 63.39%, and the parameter count was reduced by 75.13%. Additionally, we achieved optimal performance through comparative analyses with other classical and state-of-the-art models, generalization experiments, and module effectiveness tests. In conclusion, the proposed method effectively recognizes various diseases in tomato leaves and offers a practical solution for the integration of deep learning into agricultural production processes.
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