Comparison of YOLOv5 and YOLOv6 Models for Plant Leaf Disease Detection

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.

Plant phenotyping relevance

植物葉の病害を画像から検出するYOLOモデルを比較し、精度と処理時間を評価しているため、植物病徴の画像ベース表現型推定手法の技術検証が中心です。

Code and data availability

植物フェノタイピング解析を再現する公開資産であることを、入力本文と直接リンクから確認できなかったため保留しました。

No evidence-backed public reproduction asset is currently recorded.