Introduction Early and accurate detection of pumpkin leaf diseases is essential for precision agriculture; however, manual inspection remains slow, subjective, and difficult to scale in real field environments. To address these limitations, this study proposes a robust deep-learning framework for automated pumpkin leaf disease classification. Methods This study introduces DualFusion–CBAM–Stochastic, a hybrid deep-learning architecture that integrates two complementary convolutional backbones: DenseNet121 for fine-grained texture representation through dense connectivity and EfficientNetB3 for multi-scale contextual feature extraction using compound scaling. Input images are preprocessed through resizing to 224 × 224 pixels, ImageNet-based normalization, and controlled data augmentation, including horizontal and vertical flips, rotation, and zoom. Feature refinement is achieved using the Convolutional Block Attention Module (CBAM), which applies sequential channel and spatial attention, while stochastic-depth regularization improves generalization by randomly bypassing deep layers during training. Results The proposed model was trained on a balanced dataset of 2,000 images across five pumpkin leaf disease categories. Experimental evaluation using ablation studies and comparative analysis against state-of-the-art models demonstrates that the proposed architecture achieves 96% classification accuracy, outperforming existing CNN-based approaches. Discussion The results confirm that the synergistic integration of dual-backbone fusion, attention-guided refinement, and stochastic-depth regularization significantly enhances classification performance, feature interpretability, and model stability under diverse visual conditions. These findings advance automated pumpkin leaf disease diagnosis and provide a strong methodological foundation for future research in agricultural image analysis.
Bhuria et al. (2026) studied this question.