Integration of Image Enhancement Technique with DenseNet201 Architecture for Identifying Grapevine Leaf Disease

Early detection of grapevine leaf diseases is crucial for maintaining both the quality and quantity of grape production. Manual identification methods are often ineffective and prone to errors. This research aims to develop a precise and efficient method for classifying grapevine leaf diseases using Contrast Limited Adaptive Histogram Equalization (CLAHE) and the DenseNet201 Deep Convolutional Neural Network (DCNN) architecture. The research methodology involves collecting a dataset of grapevine leaf images affected by black measles, black rot, and leaf blight alongside healthy leaves. Following this, preprocessing is conducted using the CLAHE technique to enhance image quality. Then, the processed data is trained with DenseNet201. Evaluation results indicate that the proposed model achieves an overall accuracy of 99.61%, with high precision, recall, and F1-score values across all disease classes. Receiver Operating Characteristic (ROC) curve analysis shows an Area Under the Curve (AUC) of 1.00 for each class, reflecting excellent discriminatory ability. The loss and accuracy curves illustrate consistent model performance without signs of overfitting. Additionally, the confusion matrix confirms very low classification error rates. The developed model is effective and reliable for identifying grapevine leaf diseases. Future research will focus on enhancing the dataset by incorporating more data optimizing hyperparameters, and developing field applications for real-time use.