Comparison of YOLOv5 and YOLOv6 Models for Plant Leaf Disease Detection
Received: 7 February 2024 | Revised: 7 March 2024 | Accepted: 10 March 2024 | Online: 2 April 2024
Corresponding author: Ecem Iren
Abstract
Deep learning is a concept of artificial neural networks and a subset of machine learning. It deals with algorithms that train and process datasets to make inferences for future samples, imitating the human process of learning from experiences. In this study, the YOLOv5 and YOLOv6 object detection models were compared on a plant dataset in terms of accuracy and time metrics. Each model was trained to obtain specific results in terms of mean Average Precision (mAP) and training time. There was no considerable difference in mAP between both models, as their results were close. YOLOv5, having 63.5% mAP, slightly outperformed YOLOv6, while YOLOv6, having 49.6% mAP50-95, was better in detection than YOLOv5. Furthermore, YOLOv5 trained data in a shorter time than YOLOv6, since it has fewer parameters.
Keywords:
deep learning, YOLOv5, YOLOv6, plant leaf disease detection, convolutional neural networkDownloads
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