Image Segmentation Deep Learning Model for Early Detection of Banana Diseases

Abstract

Bananas are among the most widely produced perennial fruits and staple food crops that are highly affected by numerous diseases. When not managed early, Fusarium Wilt and Black Sigatoka are two of the most detrimental banana diseases in East Africa, resulting in production losses of 30% to 100%. Early detection of these banana diseases is necessary for designing proper management practices to avoid further yields and financial losses. The recent advances and successes of deep learning in detecting plant diseases have inspired this study. This study assessed a U-Net semantic segmentation deep learning model for the early detection and segmentation of Fusarium Wilt and Black Sigatoka banana diseases. This model was trained using 18,240 banana leaf and stalk images affected by these two banana diseases. The dataset was collected from the farms using mobile phone cameras with the guidance of agricultural experts and was annotated to label the images. The results showed that the U-Net model achieved a Dice Coefficient of 96.45% and an Intersection over Union (IoU) of 93.23%. The model accurately segmented areas where the banana leaves and stalks were damaged by Fusarium Wilt and Black Sigatoka diseases.