WSRD-Net: A Convolutional Neural Network-Based Arbitrary-Oriented Wheat Stripe Rust Detection Method

doi:10.3389/fpls.2022.876069

HFCAS OpenIR

	WSRD-Net: A Convolutional Neural Network-Based Arbitrary-Oriented Wheat Stripe Rust Detection Method
	Liu, Haiyun 1,2; Jiao, Lin 1,3; Wang, Rujing1,2,4 ; Xie, Chengjun1,2 ; Du, Jianming 1; Chen, Hongbo 1,2; Li, Rui 1
	2022-05-24
发表期刊	FRONTIERS IN PLANT SCIENCE
ISSN	1664-462X
通讯作者	Jiao, Lin(ljiao@ahu.edu.cn) ; Wang, Rujing(rjwang@iim.ac.cn)
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.
关键词	arbitrary-oriented convolutional neural network deep learning wheat strip rust detection
DOI	10.3389/fpls.2022.876069
关键词[WOS]	YELLOW RUST ; REFLECTANCE MEASUREMENTS ; DISEASE DIAGNOSIS
收录类别	SCI
语种	英语
WOS研究方向	Plant Sciences
WOS类目	Plant Sciences
WOS记录号	WOS:000807425200001
出版者	FRONTIERS MEDIA SA
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.hfcas.ac.cn:8080/handle/334002/131160
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.Chinese Acad Sci, Inst Intelligent Machines, Hefei Inst Phys Sci, Hefei, Peoples R China 2.Univ Sci & Technol China, Sci Isl Branch, 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).