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A Recognition Method for Rice Plant Diseases and Pests Video Detection Based on Deep Convolutional Neural Network

文献类型：期刊论文

作者Li, Dengshan1,2; Wang, Rujing2; Xie, Chengjun2; Liu, Liu1,2; Zhang, Jie2; Li, Rui1,2; Wang, Fangyuan1,2; Zhou, Man1,2; Liu, Wancai3
刊名SENSORS
出版日期2020-02-01
卷号20
关键词rice diseases and pests deep learning video detection deep convolutional neural network video metrics
DOI10.3390/s20030578
通讯作者Wang, Rujing(rjwang@iim.ac.cn) ; Xie, Chengjun(cjxie@iim.ac.cn)
英文摘要Increasing grain production is essential to those areas where food is scarce. Increasing grain production by controlling crop diseases and pests in time should be effective. To construct video detection system for plant diseases and pests, and to build a real-time crop diseases and pests video detection system in the future, a deep learning-based video detection architecture with a custom backbone was proposed for detecting plant diseases and pests in videos. We first transformed the video into still frame, then sent the frame to the still-image detector for detection, and finally synthesized the frames into video. In the still-image detector, we used faster-RCNN as the framework. We used image-training models to detect relatively blurry videos. Additionally, a set of video-based evaluation metrics based on a machine learning classifier was proposed, which reflected the quality of video detection effectively in the experiments. Experiments showed that our system with the custom backbone was more suitable for detection of the untrained rice videos than VGG16, ResNet-50, ResNet-101 backbone system and YOLOv3 with our experimental environment.
WOS关键词SEVERITY ; PRECISION ; SPOT
资助项目National Key Technology R&D Program of China[2018YFD0200300] ; National Natural Science Foundation of China[31401293] ; National Natural Science Foundation of China[31671586] ; National Natural Science Foundation of China[61773360]
WOS研究方向Chemistry ; Engineering ; Instruments & Instrumentation
语种英语
WOS记录号WOS:000517786200002
出版者MDPI
资助机构National Key Technology R&D Program of China ; National Natural Science Foundation of China
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/103619]  
专题中国科学院合肥物质科学研究院
通讯作者Wang, Rujing; Xie, Chengjun
作者单位1.Univ Sci & Technol China, Grad Sch, Sci Isl Branch, Hefei 230026, Peoples R China
2.Chinese Acad Sci, Hefei Inst Phys Sci, Inst Intelligent Machines, Hefei 230031, Peoples R China
3.Natl Agrotech Extens & Serv Ctr, Beijing 100125, Peoples R China
推荐引用方式
GB/T 7714		 Li, Dengshan,Wang, Rujing,Xie, Chengjun,et al. A Recognition Method for Rice Plant Diseases and Pests Video Detection Based on Deep Convolutional Neural Network[J]. SENSORS,2020,20.
APA Li, Dengshan.,Wang, Rujing.,Xie, Chengjun.,Liu, Liu.,Zhang, Jie.,...&Liu, Wancai.(2020).A Recognition Method for Rice Plant Diseases and Pests Video Detection Based on Deep Convolutional Neural Network.SENSORS,20.
MLA Li, Dengshan,et al."A Recognition Method for Rice Plant Diseases and Pests Video Detection Based on Deep Convolutional Neural Network".SENSORS 20(2020).

入库方式： OAI收割

来源：合肥物质科学研究院

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