Optimizing Pomegranate Disease Detection: A Comparative Analysis of Deep Learning Models

Pomegranate is a highly valuable fruit widely cultivated across various countries, yet it faces significant challenges from plant diseases that lead to considerable crop losses and diminished yields. Early detection of these diseases is crucial for enhancing productivity and minimizing economic losses. This study explores image processing and convolutional neural network (CNN) techniques for detecting pomegranate diseases. We evaluate the performance of six deep learning models, DenseNet201, MobileNetV2, Xception, VGG19, VGG16, and InceptionV3, on a dataset of pomegranate images. Xception, in particular, demonstrated exceptional computational efficiency and accuracy, achieving an impressive 97% overall accuracy. The study highlights the advantages of using CNN-based methods over traditional manual grading by experts, which often results in lower accuracy and higher financial losses. The results indicate that modern image processing techniques can significantly improve disease detection, offering a reliable and efficient alternative for pomegranate disease management. Challenges such as class imbalance and computational limitations were addressed through data augmentation and model optimization. Future research directions include exploring ensemble methods to enhance model performance, integrating real-time analysis for field applications, and optimizing models for diverse agricultural contexts. This approach promises to revolutionize pomegranate disease detection and contribute to sustainable agricultural practices.