A multi-scale cucumber disease detection method in natural scenes based on YOLOv5

Abstract

Plant diseases are the main factors affecting the agricultural production. At present, improving the efficiency of plant disease identification in natural scenarios is a crucial issue. Due to this significance, this study aims at providing an efficient detection method, which is applicable to disease detection in natural scenes. The proposed MTC-YOLOv5n method is based on the YOLOv5 model, which integrates the Coordinate Attention (CA) and Transformer in order to reduce invalid information interference in the background, and combines a Multi-scale training strategy (MS) and feature fusion network to improve the small object detection accuracy. MTC-YOLOv5n is trained and validated on a self-built cucumber disease dataset. The model size and FLOPs are respectively 4.7 MB and 6.1 G, achieving 84.9 % mAP and FPS up to 143. Compared with the advanced single-stage detection model, the experimental results show that MTC-YOLOv5n has higher detection accuracy and speed, smaller computation and model size. In addition, the proposed model is tested under the interference of strong noise conditions such as dense fog, drizzle and dark light, which shows that the model has strong robustness. Finally, the comprehensive experimental results demonstrate that MTC-YOLOv5n is lightweight, efficient and suitable for deployment to mobile terminals for disease detection in natural scenarios.

Plant phenotyping relevance

キュウリの病徴を自然画像から検出するYOLOv5ベースの画像解析手法を開発し、自作データセットで精度・速度・頑健性を検証しており、植物フェノタイピング手法が中心である。

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