YOLO26n-Based Apple Leaf Disease Detection for Precision Agriculture Using Lightweight Deep Learning and Object Detection - Darsiti Darsiti; Budiman; Dhika Wdiyanto; Tarsinah Sumarni (Bulletin of Intelligent Machines and Algorithms, 2026)
Early detection of apple leaf diseases is a critical factor in supporting agricultural productivity and minimizing losses caused by plant disease outbreaks. However, manual identification processes still have limitations in terms of accuracy, consistency, and time efficiency. This study aims to develop an apple leaf disease detection model based on object detection using YOLO26n to identify four main classes: Apple__BlackRot, Apple__CedarRust, Apple__Healthy, and Apple__Scab. The dataset was obtained from Kaggle in YOLO format, consisting of 2,754 training images and 687 validation images. The study employs a transfer learning approach with various data augmentation techniques, such as mosaic, mixup, copy-paste, rotation, translation, and HSV transformation, to enhance the model’s generalization ability. Evaluation was conducted using the Precision, Recall, mAP50, and mAP50-95 metrics. The results showed that the YOLO26n model achieved a Precision of 0.968, a Recall of 0.887, an mAP50 of 0.958, and an mAP50-95 of 0.880. The best performance was achieved on the Apple__BlackRot class with an mAP50-95 value of 0.987. The inference results also show that the model is capable of accurately localizing diseases through bounding boxes with a high level of confidence. These findings indicate that YOLO26n has great potential as an efficient and accurate lightweight model for the implementation of real-time precision agriculture-based plant disease detection systems.