A Computer Vision Approach for Banana Leaf Disease Classification

Abstract

This study investigates the potential of computer vision approach for classifying banana leaf diseases - Cordana, Pestalotiopsis, Sigatoka in the context of regional variations. We collected a dataset of banana leaf images from Habiganj, Sylhet, Bangladesh, to account for these variations and employed data augmentation techniques to enrich its size and diversity. Five pre-trained CNN architectures (VGG19, VGG16, ResNet50, MobileNetV1, MobileNetV2) were evaluated for their disease classification performance. The evaluation compared the models' performance with and without image preprocessing. Our findings highlight the exceptional performance of MobileNetV2, achieving an impressive 94.41% accuracy on raw, unprocessed data. This accuracy further improved to 96.41% after image preprocessing, demonstrating the model's robustness and resilience to variations that might be encountered in real-world applications. These results emphasize the potential of CNNs, particularly lightweight architectures like MobileNetV2, for accurate banana leaf disease classification using computer vision. This study provides valuable insights for developing future plant disease identification systems, ultimately contributing to improved disease management and promoting sustainable agricultural practices.