您好，欢迎访问广东省农业科学院 机构知识库！
广东省农业科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

OR-FCOS: an enhanced fully convolutional one-stage approach for growth stage identification of Oudemansiella raphanipes

文献类型： 外文期刊

作者： Fang, Runze 1 ; Huang, Huamao 3 ; Guo, Nuoyan 4 ; Wei, Haichuan 1 ; Wang, Shiyi 1 ; Hu, Haiying 5 ; Liu, Ming 2 ;

作者机构： 1.South China Univ Technol, Sch Future Technol, Guangzhou 511442, Guangdong, Peoples R China

2.Guangdong Acad Agr Sci, Vegetable Res Inst, Guangdong Key Lab New Technol Res Vegetables, Guangzhou 510640, Guangdong, Peoples R China

3.South China Univ Technol, Sch Phys & Optoelect, Guangzhou 510641, Guangdong, Peoples R China

4.South China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510641, Guangdong, Peoples R China

5.South China Univ Technol, Sch Civil Engn & Transportat, Guangzhou 510641, Guangdong, Peoples R China

关键词： Oudemansiella raphanipes; Object detection; MobileNetV3; Efficient multi-scale attention; NAS-FCOS; Channel pruning

期刊名称：SCIENTIFIC REPORTS （ 影响因子：3.9； 五年影响因子：4.3 ）

ISSN： 2045-2322

年卷期： 2025 年 15 卷 1 期

页码：

收录情况： SCI

摘要： Accurate identification of Oudemansiella raphanipes growth stages is crucial for understanding its development and optimizing cultivation. However, deep learning methods for this task remain unexplored. This paper introduces OR-FCOS, an enhanced fully convolutional one-stage (FCOS) approach designed to improve accuracy and efficiency in identifying these growth stages. We constructed the ORaph8K dataset, containing 8,000 images of Oudemansiella raphanipes at different growth stages, used for training and validation. The OR-FCOS uses the MobileNetV3-Large backbone with an efficient multi-scale attention (EMA) module, improving feature extraction efficiency without sacrificing accuracy. A neural architecture search (NAS)-enhanced FCOS decoder replaces both the traditional feature pyramid networks (FPN) and prediction head in FCOS, optimizing feature fusion and prediction. Integrating the complete intersection over union (CIoU) loss function addresses standard IoU limitations by factoring in aspect ratio and bounding box center distance. Channel pruning further reduces the decoder's parameters, decreasing model size and computational requirements while maintaining precision. The enhanced algorithm achieved a mean average precision (mAP) of 89.4% (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {mAP}_{50}$$\end{document}) and 78.3% (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {mAP}_{50:95}$$\end{document}), while the number of model parameters was reduced to 9.9 M, the model size was reduced to 40.1 MB, and the number of floating point operations per second (FLOPs) was reduced to 31.2 G. These results show that OR-FCOS accurately and efficiently identifies the growth stages of Oudemansiella raphanipes. By installing cameras in cultivation facilities, our algorithm enables automated and real-time monitoring, thereby supporting large-scale factory-based production of the fungus.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：广东省农业科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台