YOLOv11-based detection and classification of diseases in Siamese orange fruit using digital images

Abstract

Diseases in Siamese orange fruit are one of the factors that can reduce the quality and yield of agricultural production. Manual disease identification requires considerable time and depends heavily on human observation skills; therefore, an automated system capable of detecting diseases quickly and accurately is needed. This study aims to implement the YOLOv11 model for detecting and classifying diseases in Siamese orange fruit based on digital images. The dataset used consisted of four classes, namely anthracnose, citrus canker, scab, and healthy, with a total of 627 images divided into training, validation, and testing datasets. The study utilized 20 variations of data augmentation, and DataV18 produced the best performance. The training process was conducted using the YOLOv11s architecture with 200 epochs and various data augmentation techniques. Based on the testing results, the model achieved a precision of 70.5%, recall of 61.2%, F1-score of 65.5%, mAP@0.5 of 61.8%, and mAP@0.5:0.95 of 44.8%. The results indicate that the YOLOv11 model has a fairly good capability in detecting diseases in Siamese orange fruit based on digital images and has the potential to be applied in the development of artificial intelligence-based plant disease detection systems.