(untitled) (Physical Education, Health and Social Sciences, 2025)
This study proposes a deep learning-based method for the automated identification of grape leaf diseases, focusing on the classification of infected and healthy grape leaves. A Deep Convolutional Neural Network (CNN) model was designed and trained on a curated dataset comprising 1,180 diseased and 1,000 healthy grape leaf images. Data augmentation techniques, including rotation, flipping, scaling, noise injection, gamma correction and principal component analysis (PCA), were employed to improve model generalization and reduce overfitting. The model was trained using optimized hyperparameters such as epoch, batch size, and dropout. Experimental results achieved a classification accuracy of 97.25%, with precision, recall, and F1-score of 95.16%, 100%, and 97.52% for infected leaves, and 100%, 94%, and 96.91% for Healthy leaves, respectively. The proposed model demonstrates reliable performance and can be integrated into precision agriculture systems for early disease detection and crop management.