Comparative Evaluation of DenseNet121 and DenseNet201 for Automated Detection of Major Fava Bean Leaf Diseases using Field-collected Images

Foliar diseases like Chocolate spot, Rust and Gall threaten fava bean yield and quality. Traditional visual inspection is slow and error-prone, so automated deep learning approaches offer faster and more accurate disease detection.

This study evaluates the performance of the deep learning architectures DenseNet121 and DenseNet201 for automated fava bean leaf disease classification using a field-acquired dataset of 8,021 RGB images across four classes: Healthy, Rust, Gall and Chocolate spot. Both models were trained, validated and tested under identical preprocessing, augmentation and hyperparameter settings. Evaluation metrics included accuracy, precision, recall, F1-score, MCC, ROC-AUC, Precision-Recall curves and confusion matrices, supported by qualitative confidence-score analysis.

Both models demonstrated high and stable performance, with DenseNet121 achieving 96.45% accuracy and DenseNet201 achieving 96.01% accuracy. DenseNet201 converged faster and showed slightly stronger recall for Rust, whereas DenseNet121 achieved marginally higher overall accuracy and MCC. High confidence scores and low misclassification rates across all classes highlight the effectiveness of DenseNet architectures for reliable, real-time disease detection in precision agriculture.