Deep Learning-Based Approach for Pomegranate Fruit Bacterial Blight Disease Detection using Convolutional Neural Networks

Pomegranate fruit bacterial blight disease is a serious threat to pomegranate orchards worldwide, causing significant economic losses. Accurate and timely detection of this disease is crucial for implementing effective control measures. In this paper, propose a deep learning-based approach for the detection and classification of pomegranate fruit bacterial blight disease. Collected a comprehensive dataset of infected and healthy pomegranate fruit images and annotated them to identify the disease regions. Our proposed model leverages the power of convolutional neural networks (CNNs) to automatically learn discriminative features from the images. Trained the CNN model on the dataset and achieved promising results, with an accuracy of 92% in disease detection and 89% in disease classification. The developed model demonstrates great potential for practical implementation in automated disease detection systems, aiding farmers in making informed decisions regarding disease management and reducing crop losses.