GRAPEVINE DISEASE DETECTION USING AN OPTIMIZED DEEP NEURAL NETWORK FOR SMART AGRICULTURE

Abstract

Grapevines are economically vital crops but are highly susceptible to fungal, bacterial, and viral diseases that threaten yield and quality. Traditional detection methods rely on manual inspection, are time-consuming, prone to human error, and often delay intervention. This study introduces a lightweight convolutional neural network (CNN) architecture specifically designed for accurately and efficiently detecting grapevine leaf diseases—including Black Rot, ESCA, and Leaf Blight—based on image classification. The proposed model integrates optimized residual blocks, batch normalization, dropout layers, and global average pooling to maximize accuracy while minimizing computational complexity. This lightweight design makes it well-suited for deployment on edge devices such as drones and mobile systems used in precision agriculture. A comprehensive data augmentation strategy was applied during training to simulate real-world variability and enhance generalization. The model was trained using 9,027 labeled grape leaf images from a publicly available grape disease image dataset, and it achieved 99.8% overall accuracy with near perfict precision, recall, F1-score, and area under the ROC curve (AUC) across all classes. These findings highlight the practical potential or real-time, scalable and sustainable disease monitoring in smart vineyard management systems.