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AgriPest: A Large-Scale Domain-Specific Benchmark Dataset for Practical Agricultural Pest Detection in the Wild

文献类型：期刊论文

作者Wang, Rujing2,3; Liu, Liu2,3,4; Xie, Chengjun2,3; Yang, Po1; Li, Rui2,3,4; Zhou, Man2,3,4
刊名SENSORS
出版日期2021-03-01
卷号21
关键词pest detection agricultural dataset AgriPest deep learning
DOI10.3390/s21051601
通讯作者Liu, Liu(liuliu66@mail.ustc.edu.cn)
英文摘要The recent explosion of large volume of standard dataset of annotated images has offered promising opportunities for deep learning techniques in effective and efficient object detection applications. However, due to a huge difference of quality between these standardized dataset and practical raw data, it is still a critical problem on how to maximize utilization of deep learning techniques in practical agriculture applications. Here, we introduce a domain-specific benchmark dataset, called AgriPest, in tiny wild pest recognition and detection, providing the researchers and communities with a standard large-scale dataset of practically wild pest images and annotations, as well as evaluation procedures. During the past seven years, AgriPest captures 49.7K images of four crops containing 14 species of pests by our designed image collection equipment in the field environment. All of the images are manually annotated by agricultural experts with up to 264.7K bounding boxes of locating pests. This paper also offers a detailed analysis of AgriPest where the validation set is split into four types of scenes that are common in practical pest monitoring applications. We explore and evaluate the performance of state-of-the-art deep learning techniques over AgriPest. We believe that the scale, accuracy, and diversity of AgriPest can offer great opportunities to researchers in computer vision as well as pest monitoring applications.
资助项目National Natural Science Foundation of China (NSFC)[61773360] ; National Natural Science Foundation of China (NSFC)[31671586] ; Major Special Science and Technology Project of Anhui Province[201903a06020006] ; Innovate UK (UK-China: Precision for Enhancing Agriculture Productivity)[671197]
WOS研究方向Chemistry ; Engineering ; Instruments & Instrumentation
语种英语
WOS记录号WOS:000628584700001
出版者MDPI
资助机构National Natural Science Foundation of China (NSFC) ; Major Special Science and Technology Project of Anhui Province ; Innovate UK (UK-China: Precision for Enhancing Agriculture Productivity)
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/120949]  
专题中国科学院合肥物质科学研究院
通讯作者Liu, Liu
作者单位1.Univ Sheffield, Dept Comp Sci, Sheffield S1 1DA, S Yorkshire, England
2.Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, Peoples R China
3.Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230031, Peoples R China
4.Univ Sci & Technol China, Grad Sch, Sci Isl Branch, Hefei 230026, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Rujing,Liu, Liu,Xie, Chengjun,et al. AgriPest: A Large-Scale Domain-Specific Benchmark Dataset for Practical Agricultural Pest Detection in the Wild[J]. SENSORS,2021,21.
APA Wang, Rujing,Liu, Liu,Xie, Chengjun,Yang, Po,Li, Rui,&Zhou, Man.(2021).AgriPest: A Large-Scale Domain-Specific Benchmark Dataset for Practical Agricultural Pest Detection in the Wild.SENSORS,21.
MLA Wang, Rujing,et al."AgriPest: A Large-Scale Domain-Specific Benchmark Dataset for Practical Agricultural Pest Detection in the Wild".SENSORS 21(2021).

入库方式： OAI收割

来源：合肥物质科学研究院

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