Real-Time Pomegranate Leaf Disease Detection Using Deep Learning Models

Abstract

Pomegranate is a commercially valuable horticulture crop whose productivity is significantly affected by leaf diseases, particularly Bacterial Blight caused by Xanthomonas axonopodis pv. punicae. Early detection is crucial for preventing widespread infection and minimizing economic losses. Conventional diagnostic methods rely on visual examination by experts, which is subjective, labour-intensive and prone to delays. This paper presents a Convolutional Neural Network (CNN)–based deep learning approach for automated detection of pomegranate leaf diseases from image datasets. The proposed architecture processes raw leaf images, performs feature extraction and classifies them into healthy or diseased categories. Data augmentation, normalization and regularization techniques are applied to enhance model robustness and reduce overfitting. Experimental results demonstrate high classification accuracy and improved precision compared to traditional machine learning models. The trained model is further integrated into a web-based interface for real-time prediction, enabling farmers and agricultural practitioners to upload leaf images using low-cost devices. The system supports rapid decision-making and contributes to precision agriculture by providing a scalable, reliable and automated disease identification solution.