您好，欢迎访问天津市农业科学院 机构知识库！
天津市农业科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

The Gray Mold Spore Detection of Cucumber Based on Microscopic Image and Deep Learning

文献类型： 外文期刊

作者： Li, Kaiyu 1 ; Zhu, Xinyi 1 ; Qiao, Chen 1 ; Zhang, Lingxian 1 ; Gao, Wei 3 ; Wang, Yong 3 ;

作者机构： 1.China Agr Univ, Beijing 100083, Peoples R China

2.Minist Agr & Rural Affairs, Key Lab Agr Informationizat Standardizat, Beijing 100083, Peoples R China

3.Tianjin Acad Agr Sci, Inst Plant Protect, Tianjin 300384, Peoples R China

期刊名称：PLANT PHENOMICS （ 影响因子：6.5； 五年影响因子：7.5 ）

ISSN： 2643-6515

年卷期： 2023 年 5 卷

页码：

收录情况： SCI

摘要： Rapid and accurate detection of pathogen spores is an important step to achieve early diagnosis of diseases in precision agriculture. Traditional detection methods are time-consuming, laborious, and subjective, and image processing methods mainly rely on manually designed features that are difficult to cope with pathogen spore detection in complex scenes. Therefore, an MG-YOLO detection algorithm (Multi-head self-attention and Ghost-optimized YOLO) is proposed to detect gray mold spores rapidly. Firstly, Multi-head self-attention is introduced in the backbone to capture the global information of the pathogen spores. Secondly, we combine weighted Bidirectional Feature Pyramid Network (BiFPN) to fuse multiscale features of different layers. Then, a lightweight network is used to construct GhostCSP to optimize the neck part. Cucumber gray mold spores are used as the study object. The experimental results show that the improved MG-YOLO model achieves an accuracy of 0.983 for detecting gray mold spores and takes 0.009 s per image, which is significantly better than the state-of-the-art model. The visualization of the detection results shows that MG-YOLO effectively solves the detection of spores in blurred, small targets, multimorphology, and high-density scenes. Meanwhile, compared with the YOLOv5 model, the detection accuracy of the improved model is improved by 6.8%. It can meet the demand for high-precision detection of spores and provides a novel method to enhance the objectivity of pathogen spore detection.

  • 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：天津市农业科学院

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