Enhanced YOLOv8 with Lightweight and Efficient Detection Head for for Detecting Rice Leaf Diseases

Abstract

Abstract Detecting rice leaf diseases is essential for agricultural stability and crop health. However, the diversity of these diseases, their uneven distribution, and complex field environments create challenges for precise, multi-scale detection. While YOLO object detection algorithms show strong performance in automated detection, further optimization is needed. This paper presents G-YOLO, a novel architecture that combines a Lightweight and Efficient Detection Head (LEDH) with Multi-scale Spatial Pyramid Pooling Fast (MSPPF). The LEDH enhances detection speed by simplifying the network structure while maintaining accuracy, reducing computational demands. The MSPPF improves the model’s ability to capture intricate details of rice leaf diseases at various scales by fusing multi-level feature maps. On the RiceDisease dataset, G-YOLO surpasses YOLOv8n with 4.4% higher mAP@0.5, 3.9% higher mAP@0.75, and a 13.1% increase in FPS, making it well-suited for resource-constrained devices due to its efficient design.

Plant phenotyping relevance

イネ葉の病害状態を画像から検出するYOLOベース手法を開発・評価しており、植物病害表現型の取得・推定が中心的な貢献である。

Code and data availability

The paper's rice leaf disease detection experiments were run on a publicly available Kaggle dataset (RiceDisease, 850 images of Bacterial Leaf Blight, Blast, and Brown Spot), which is a paper-specific public phenotype image asset. No author analysis code, trained model checkpoints, or other paper-specific assets are公开;