YOLOv10-RLF:A lightweight small target grape leaf disease detection method based on improved YOLOv10

Abstract

Abstract Grapevine crops often suffer from various diseases, posing significant challenges to agricultural production. The diverse morphology and dense distribution of grape leaf diseases complicate identification efforts. As demand for intelligent devices grows, mobile platforms require more efficient neural network algorithms. To address these challenges, we propose a lightweight grape leaf disease detection model, YOLOv10-RLF, based on the YOLOv10n architecture. We introduce the C2f-RVB module, which separates token and channel mixing to enhance feature extraction and reduce redundancy, achieving a 21.74% reduction in parameters and a 19.54% reduction in Giga Floating-point Operations Per Second(GFLOPs). Additionally, we propose a lightweight detection head, v10_LSCD, utilizing shared convolution to further decrease the model's parameters and operational load. To improve detection accuracy, especially for small targets, we replace the SPPF module with a Feature Pyramid Shared Conv (FPSC) module, enhancing multi-scale feature fusion. We also optimize the loss function using SIOU to boost convergence speed and computational accuracy. Experimental results demonstrate that our model reduces parameters and operations by 26.2% and 30.4%, respectively, while compressing model size by 36.4%, all without sacrificing detection accuracy. This research provides a technical foundation for mobile detection of grapevine diseases and supports precise variable applications in agriculture.

Code and data availability

The paper uses a custom grape leaf disease dataset (4290 images from Jiangbei Vineyard and Plant Village, YOLO-format LabelImg annotations) and trains a YOLOv10-RLF model, but no public dataset, code, or model deposit is provided. The Data availability statement says data are available only from the corresponding作者upon

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