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Kernel PCA was used to capture nonlinear patterns, enhancing the models\u2019 discriminative power. Classification was performed using the XGBoost algorithm, optimized through the Slime Mould Algorithm (SMA) for feature selection and hyperparameter tuning. Experimental results demonstrate that MobileNet achieved the highest performance with an accuracy of 99.08% and specificity of 99.39%, while DenseNet169 showed comparable effectiveness. The interpretability of the proposed framework was validated using Local Interpretable Model-Agnostic Explanations (LIME), which visually highlighted the critical regions of leaf images influencing disease classification decisions. 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