Multi-Class Banana Leaf Disease Detection via KHO-YOLOv8

Abstract

Abstract Plant diseases initiate major agricultural challenges because they lead to 16\% of worldwide crop loss in farms. The vulnerability of bananas to diseases including Xanthomonas Wilt and Sigatoka leaf spot puts severe threats to food security because of their extensive damage potential. These diseases possess the risk of damage to the complete harvest so their impact can reach 100%. While deep learning models, particularly YOLO-based architecture, have demonstrated success in plant disease identification, key research gaps remain. One major challenge is the lack of large-scale, annotated datasets for banana leaf diseases, limiting the development and evaluation of robust AI models. Addressing these challenges is crucial, and this study aims to do so by creating a large dataset and developing a robust disease detection model. The dataset comprises more than 5000 samples categorized into three classes: Healthy, Xanthomonas Wilt infected, and Sigatoka leaf spot infected. This study examines a novel framework by employing advanced optimization techniques such as Krill Herd Optimization (KHO) for YOLOv8 and its variants. Our research findings highlight the exceptional performance of the KHO-YOLOv8 model, achieving an impressive accuracy of 96.47%.

Code and data availability

The paper states its banana leaf disease dataset (5,000 annotated images) and code are publicly available on GitHub, but no concrete repository URL or identifier is provided in the supplied text, and the only allowed URL is an unrelated cited reference. The claim of public availability is explicit, but the asset is not