The Accuracy and Efficiency of YOLO Algorithms in Identifying Plant Leaf Diseases
DOI:
https://doi.org/10.69968/ijisem.2026v5i2250-259Keywords:
Plant Disease Detection, Deep Learning, YOLOv5, YOLOv7, YOLOv8Abstract
Plant diseases are one of the biggest challenges of world agriculture, leading to enormous output losses and economic damages. Early and accurate detection of these diseases may help to increase crop yield, improve resource efficiency, decrease costs and environmental impact and assist the production of high-quality food. In recent years, deep learning (especially computer vision approaches) has become a strong tool for a number of tasks such as picture classification, segmentation and object detection. Such techniques include the You Only Look Once (YOLO) family of neural networks, a state-of-the-art technology for accurate object detection. In this work, we use YOLOv5, YOLOv7 and YOLOv8 models for citrus disease detection with the CCL’20 dataset. During training, a number of data augmentation techniques are used to improve the model performance, such as picture translation, scaling, flipping and mosaic augmentation. The model performance was evaluated using the Mean Average Precision (mAP) for Intersection over Union thresholds from 50% to 95% (mAP@50–95). The results showed that the YOLOv8 model performed better than the other variations, with significant improvements compared to the benchmarks reported in previous studies. After hyper-parameter adjustment, the improved model reached a mAP@50-95 of 96.1% on the test set for detection of the citrus diseases. The model attained the mAP@50-95 of 95.3%, 96.0% and 97.0% for Anthracnose, Melanose and Bacterial Brown Spot respectively for each disease. Furthermore, the model could reliably identify both single and many cases of the same and different diseases inside a single image, illustrating the robustness of recent YOLO architectures. Finally, the trained YOLOv8 model has been successfully installed into the Roboflow platform which is ready for practical applications in citrus disease monitoring.
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