Digital framework for georeferenced multiplatform surveillance of banana wilt using human in the loop AI and YOLO foundation models.

Abstract

Bananas (Musa spp.) are a critical global food crop, providing a primary source of nutrition for millions of people. Traditional methods for disease monitoring and detection are often time-consuming, labor-intensive, and prone to inaccuracies. This study introduces an AI-powered multiplatform georeferenced surveillance system designed to enhance the detection and management of banana wilt diseases. We developed and evaluated several deep learning foundation models, including YOLO-NAS, YOLOv8, YOLOv9, and Faster-RCNN to perform accurate disease detection on both platforms. Our results demonstrate the superior performance of YOLOv9 in detecting healthy, Fusarium Wilt and Xanthomonas Wilt diseased plants in aerial images, achieving high mAP@50, precision and recall metrics ranging from 55 to 86%. In terms of ground level images, we organized the dataset based on disease occurrence in Africa, Latin America, India, Asia and Australia. For this platform, YOLOv8 outperforms the rest and achieves mAP@50, precision and recall between 65 and 99% depending on the plant part and region. Additionally, we incorporated Explainable AI techniques, such as Gradient-weighted Class Activation Mapping, to enhance model transparency and trustworthiness. Human in the Loop Artificial Intelligence was also utilized to enhance the ground level model's predictions.

Plant phenotyping relevance

バナナ個体の健全・萎凋病状態を航空・地上画像から推定する深層学習モデルを開発・評価しており、植物病害状態の画像ベースフェノタイピング手法が中心です。

Code and data availability

The paper describes banana wilt image datasets and YOLO/Faster-RCNN models, but the Data availability statement only offers them from the corresponding author upon request; no public repository, code deposit, or authors' public URL for the datasets, models, or analysis code is provided in the supplied blocks. The Tumau

No evidence-backed public reproduction asset is currently recorded.