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CC BY V4.0, Open Access
Journal article
RSD-YOLO: An improved YOLOv7-tiny framework for oat disease severity identification with integration of ReXNet and decoupled head
Smart agricultural technology, Vol.12, 101433
2025
Abstract
In precision agriculture, accurate and timely identification of plant disease severity is essential for optimizing crop yield and health. However, current methods often face challenges such as high computational cost and reduced accuracy in resource-constrained environments, limiting their practical use on farms. To address these limitations, we propose RSD-YOLO, an improved YOLOv7-tiny model that integrates a Regularized Xception-based Network (ReXNet), a Slim-Neck module, and a Decoupled Head—together forming the RSD design. We construct a dataset of 1,010 oat leaf images, categorized into five severity levels and annotated by experts. RSD-YOLO achieves 91.6% precision, 90.8% recall, and 88.5% mAP@0.5, significantly outperforming YOLOv7-tiny by up to 10%, while maintaining a computational cost of only 11.2 GFLOPs. Recent studies have applied lightweight models such as EfficientSAM and SwiftFormer for crop health monitoring on drones and edge devices. However, these models often struggle to balance accuracy and efficiency. In contrast, RSD-YOLO achieves higher performance with lower computational cost, making it well-suited for real-time deployment in agricultural environments.
Details
- Title
- RSD-YOLO: An improved YOLOv7-tiny framework for oat disease severity identification with integration of ReXNet and decoupled head
- Authors/Creators
- Yongquan Zhang - Zhejiang University of Finance and EconomicsYiwei Xu - Zhejiang University of Finance and EconomicsTaosheng Xu - Hefei Institutes of Physical ScienceChangmiao Wang - Shenzhen Research Institute of Big DataChengdao Li - Murdoch University, Centre for Crop and Food InnovationHai Wang - Murdoch University, Centre for Water, Energy and Waste
- Publication Details
- Smart agricultural technology, Vol.12, 101433
- Publisher
- Elsevier B.V.
- Number of pages
- 13
- Identifiers
- 991005815048407891
- Copyright
- © 2025 The Author(s).
- Murdoch Affiliation
- School of Engineering and Energy; Centre for Crop and Food Innovation; Centre for Water, Energy and Waste; Food Futures Institute
- Language
- English
- Resource Type
- Journal article
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