Knowledge Management System of Hefei Institute of Physical Science,CAS
WSRD-Net: A Convolutional Neural Network-Based Arbitrary-Oriented Wheat Stripe Rust Detection Method | |
Liu, Haiyun1,2; Jiao, Lin1,3; Wang, Rujing1,2,4; Xie, Chengjun1,2; Du, Jianming1; Chen, Hongbo1,2; Li, Rui1 | |
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 | |
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). |
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