Banana Leaf Diseases Prediction Using Deep Learning

Banana (Musa spp.) is one of the world’s most economically significant fruit crops, cultivated in over 130 countries and serving as a staple food for hundreds of millions. Banana plantations are continually threatened by a range of fungal, bacterial, and viral diseases — including Black Sigatoka, Yellow Sigatoka, Panama Wilt (Fusarium Wilt), Banana Bunchy Top Virus (BBTV), Anthracnose, and Cordana Leaf Spot — that result in severe yield losses. Conventional disease diagnosis relies on the visual expertise of agronomists, which is time-consuming and geographically constrained. This paper presents a comprehensive review of deep learning-based approaches for banana leaf disease detection, synthesizing research published between 2019 and 2025. We survey state-of-the-art architectures including VGG16/19, ResNet50, InceptionV3, MobileNetV2, EfficientNetB0, and YOLO variants. Our findings indicate that custom CNN models and hybrid attention-based architectures achieve the highest reported accuracies — up to 98.92% — establishing deep learning as a transformative tool for modern precision agriculture.