Explainable VGG16 transfer learning with SHAP and grad-CAM for wax moth pest and infestation detection in honeybee apiaries using imaging data

This study presents an adaptive framework for the incipient detection of wax moth (Galleria mellonella) outbreaks in honeybee hives by integrating deep learning and machine learning within smart farming systems. A custom dataset of 3252 high-resolution images collected from honey bee apiaries in Rahim Yar Khan, Pakistan (May–August 2024) encompasses labeled samples of healthy hives and infestation, further categorized into stages (Larva, Pupa, and Adult), enabling hierarchical classification as Main and Subclass. Features were extracted incorporating Transfer Learning with a pre-trained VGG16 network, followed by training classical and ensemble machine learning classifiers, comprising Random Forest, Logistic Regression, Support Vector Machine, K-Nearest Neighbours, and advanced Voting and Stacking ensembles. Performance was evaluated utilizing quantitative measures, notably accuracy, precision, recall, F1-score, specificity, MCC, and ROC AUC, with validation via ten-fold cross-validation. For Main class, the proposed XAI-HoneyNet ensemble attained test accuracy of 0.99, ROC AUC of 0.9994, and mean tenfold cross-validation accuracy of 0.9892. Alongside this, Cohen’s Kappa was 0.9719, the one-sample t-test yielded $$t = 225.8625$$ ($$p < 0.0001$$), and the ANOVA comparing Voting and Stacking classifiers reported $$F = 0.0184$$ ($$p = 0.8936$$). For subclass categorization, XAI-PestNet secured 96% accuracy with strong MCC and ROC AUC scores. To ensure model transparency and interpretability, SHapley Additive exPlanations (SHAP) and Gradient-weighted Class Activation Mapping (Grad-CAM) were employed, showcasing critical features and visual cues that drove predictions. To conclude, the proposed XAI-HoneyNet and XAI-PestNet offer a smart hybrid explainable AI framework for hierarchical infestation detection and pest classification in precision agriculture.