您好，欢迎访问安徽省农业科学院 机构知识库！

YOLO-CG-HS: A lightweight spore detection method for wheat airborne fungal pathogens

文献类型： 外文期刊

作者： Cheng, Tao 1 ; Zhang, Dongyan 1 ; Gu, Chunyan 2 ; Zhou, Xin-Gen 3 ; Qiao, Hongbo 4 ; Guo, Wei 4 ; Niu, Zhen 1 ; Xie, Jiyuan 1 ; Yang, Xue 2 ;

作者机构： 1.Northwest A&F Univ, Coll Mech & Elect Engn, Yangling 712100, Shaanxi, Peoples R China

2.Anhui Acad Agr Sci, Inst Plant Protect & Agroprod Safety, Hefei 230031, Peoples R China

3.Texas A&M AgriLife Res Ctr, Plant Pathol Lab, 1509 Aggie Dr, Beaumont, TX 77713 USA

4.Henan Agr Univ, Sci Coll Informat & Management, Zhengzhou 450000, Peoples R China

关键词： Lightweight model; Wheat; Air-borne disease; Spore concentration; Detection

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2024 年 227 卷

页码：

收录情况： SCI

摘要： The rapid, accurate and real-time online detection of spore concentration of various airborne pathogens in field crops is of great significance in guiding agricultural producers scientifically, enabling them to forecast disease development and implement timely preventive and control measures. This study presents a quantitative spore detection method for two prevalent wheat airborne fungal diseases using the YOLO-CG-HS lightweight model. Initially, the lightweight Context Guided module (CG) is integrated into the original Backbone of YOLOv5s to enhance the capture of global and edge information in spore images. Subsequently, the High-level Screeningfeature Pyramid Networks (HS-FPN) module is incorporated into the Head to better integrate multi-scale feature information of spores, thereby improving the model's detection performance and ability to capture spore microtargets. The model's robustness is then tested across various scenarios, including different shapes, densities, and complex backgrounds. Results indicate that the inclusion of both the CG module and the HS-FPN module into the original baseline model significantly reduces the number of model parameters to only 1.21 M. The model's average precision (mAP) stands at 95.9 %, with an FPS of 152.5, maintaining performance levels similar to the original model. Moreover, the designed model effectively addresses the challenge of identifying difficult and missed cases resulting from spore adhesion and overlap in various airborne wheat diseases. The YOLO-CG-HS lightweight model developed in this study accurately detects various types of pathogen spores while balancing parameters, efficiency, and accuracy. This offers crucial technical support for the model migration and application of low-cost and high-precision embedded field spore capture instruments.

  • 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：安徽省农业科学院

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