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A novel Decentralized Federated Learning (DFL) framework is proposed to enhance crop disease detection by improving model accuracy, convergence speed, and robustness while preserving data privacy across heterogeneous environments. This approach leverages validation loss for peer model sharing and local training correction, demonstrating superior performance over traditional centralized FL in agricultural applications.