An Interpretable, Imbalance-Aware Ensemble for Citrus Leaf Disease Classification using DenseNet169 and MobileNetV2

Citrus leaf diseases pose a significant threat to agricultural productivity, particularly in regions dependent on smallholder farming. Early and accurate detection is essential, yet traditional diagnostic methods are labor-intensive and prone to subjectivity. This study proposes a logit-level weighted ensemble framework integrating DenseNet169 and MobileNetV2 for automated citrus disease classification under class imbalance conditions. A publicly available dataset of 594 images across four classes was utilized, employing a stratified 70/30 train-validation split and class-weighted cross-entropy loss to address imbalance. Results show that MobileNetV2 achieved the highest macro F1-score (0.9417), outperforming DenseNet169 (0.9327) and the proposed ensemble (0.9283). This indicates that a well-optimized single model can outperform ensemble methods in terms of peak accuracy, particularly when predictions are highly correlated. However, the ensemble demonstrated smoother convergence and more stable validation performance, emphasizing its strength in improving training stability and prediction consistency. Grad-CAM visualization confirmed that models focused on biologically relevant lesion regions. The framework was implemented using CPU-based computation, demonstrating feasibility in resource-constrained environments. Overall, ensemble learning enhances stability even when peak accuracy gains are limited.