中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 合肥物质科学研究院
  3. 中国科学院合肥物质科学研究院
WSRD-Net: A Convolutional Neural Network-Based Arbitrary-Oriented Wheat Stripe Rust Detection Method

文献类型：期刊论文

作者Liu, Haiyun1,2; Jiao, Lin2,3; Wang, Rujing1,2,4; Xie, Chengjun1,2; Du, Jianming2; Chen, Hongbo1,2; Li, Rui2
刊名FRONTIERS IN PLANT SCIENCE
出版日期2022-05-24
卷号13
ISSN号1664-462X
关键词arbitrary-oriented convolutional neural network deep learning wheat strip rust detection
DOI10.3389/fpls.2022.876069
通讯作者Jiao, Lin(ljiao@ahu.edu.cn) ; Wang, Rujing(rjwang@iim.ac.cn)
英文摘要Wheat stripe rusts are responsible for the major reduction in production and economic losses in the wheat industry. Thus, accurate detection of wheat stripe rust is critical to improving wheat quality and the agricultural economy. At present, the results of existing wheat stripe rust detection methods based on convolutional neural network (CNN) are not satisfactory due to the arbitrary orientation of wheat stripe rust, with a large aspect ratio. To address these problems, a WSRD-Net method based on CNN for detecting wheat stripe rust is developed in this study. The model is a refined single-stage rotation detector based on the RetinaNet, by adding the feature refinement module (FRM) into the rotation RetinaNet network to solve the problem of feature misalignment of wheat stripe rust with a large aspect ratio. Furthermore, we have built an oriented annotation dataset of in-field wheat stripe rust images, called the wheat stripe rust dataset 2021 (WSRD2021). The performance of WSRD-Net is compared to that of the state-of-the-art oriented object detection models, and results show that WSRD-Net can obtain 60.8% AP and 73.8% Recall on the wheat stripe rust dataset, higher than the other four oriented object detection models. Furthermore, through the comparison with horizontal object detection models, it is found that WSRD-Net outperforms horizontal object detection models on localization for corresponding disease areas.
WOS关键词YELLOW RUST ; REFLECTANCE MEASUREMENTS ; DISEASE DIAGNOSIS
WOS研究方向Plant Sciences
语种英语
出版者FRONTIERS MEDIA SA
WOS记录号WOS:000807425200001
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/131159]  
专题中国科学院合肥物质科学研究院
通讯作者Jiao, Lin; Wang, Rujing
作者单位1.Univ Sci & Technol China, Sci Isl Branch, Hefei, Peoples R China
2.Chinese Acad Sci, Inst Intelligent Machines, Hefei Inst Phys Sci, Hefei, Peoples R China
3.Anhui Univ, Sch Internet, Hefei, Peoples R China
4.Anhui Univ, Inst Phys Sci & Informat Technol, Hefei, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Haiyun,Jiao, Lin,Wang, Rujing,et al. WSRD-Net: A Convolutional Neural Network-Based Arbitrary-Oriented Wheat Stripe Rust Detection Method[J]. FRONTIERS IN PLANT SCIENCE,2022,13.
APA Liu, Haiyun.,Jiao, Lin.,Wang, Rujing.,Xie, Chengjun.,Du, Jianming.,...&Li, Rui.(2022).WSRD-Net: A Convolutional Neural Network-Based Arbitrary-Oriented Wheat Stripe Rust Detection Method.FRONTIERS IN PLANT SCIENCE,13.
MLA Liu, Haiyun,et al."WSRD-Net: A Convolutional Neural Network-Based Arbitrary-Oriented Wheat Stripe Rust Detection Method".FRONTIERS IN PLANT SCIENCE 13(2022).

入库方式： OAI收割

来源：合肥物质科学研究院

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。