Cotton Leaf Disease Detection via Dual-Backbone CNN-Transformer Fusion with Quantitative XAI Comparison

Abstract

Deep learning has shown promise for cotton leaf disease detection, yet two critical gaps remain. First, most studies rely on a single model (Convolutional Neural Network-CNN or Transformer) and do not explore how to effectively fuse these complementary architectures. Second, eXplainable AI (XAI) methods are often used qualitatively, lacking objective benchmarks to guide method selection. To address these gaps, we evaluate six backbone models, comprising four CNNs (ResNet50, EfficientNet-B0, DenseNet121, and MobileNetV2) and two Vision Transformers (ViT-Base and DeiT-Small), on the Kaggle cotton leaf disease dataset, which contains 1711 images across four classes. We then systematically investigate five CNN–Transformer fusion strategies, namely concatenation, attention, weighted, ensemble, and variance-based fusion, to identify the most effective approach for disease classification. The best-performing individual models are DenseNet121 (92.40% accuracy) and ViT-Base (96.49% accuracy). Classification metrics include accuracy, balanced accuracy, precision/recall, F1-score, Cohen’s kappa, MCC, AUC, bootstrap confidence intervals, and McNemar tests. Computational efficiency (FLOPs, inference time, model size) is also reported. Concatenation fusion achieves the highest performance (accuracy = 99.42%, 95% CI: 98.2–100%, weighted F1 = 0.994, MCC = 0.992). For explainability, we quantitatively compare six XAI techniques, GradCAM, GradCAM++, ScoreCAM, LayerCAM, EigenCAM, and AblationCAM, using the pointing game, IoU, AUC, and localization accuracy. EigenCAM yields the best overall explainability score. This study demonstrates that simple feature concatenation between dual backbones (CNN + Transformer) is highly effective for cotton leaf disease detection and provides a benchmark for XAI method selection in plant pathology.