Recognition and counting of wheat mites in wheat fields by a three-step deep learning method
文献类型：期刊论文
| 作者 | Chen, Peng2,3,5; Li, WeiLu2,3,5; Yao, SiJie2,3,5; Ma, Chun1,2,3,5; Zhang, Jun6 ; Wang, Bing7; Zheng, ChunHou2,3,5; Xie, ChengJun4; Liang, Dong2,3,5
|
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2021-05-21 |
| 卷号 | 437 |
| 关键词 | Pest identification Pest counting Convolutional neural network Region proposal network |
| ISSN号 | 0925-2312 |
| DOI | 10.1016/j.neucom.2020.07.140 |
| 通讯作者 | Ma, Chun(minnie2069@163.com) ; Wang, Bing(bingwang@ustc.edu) ; Liang, Dong() |
| 英文摘要 | The wheat mite always causes major damage in wheat plants and results in significant yield losses. Therefore, detecting wheat mites can provide important information, such as pest population dynamics and integrated pest management by monitoring wheat mite populations. However, the automatic classification and counting of wheat mites from images taken from crop fields are more difficult than those obtained under laboratory conditions, due to complicated background in crop fields, light instability and small wheat mites in images. Furthermore, the manual identification of wheat mites is very timeconsuming and complex. Deep learning technique provides an efficiently automated way for address the issue. This paper proposes a three-step deep learning method to identify and count wheat mites from digital images. First, original large images are separated into smaller images as datasets. Then, the small images are labeled and then enlarged so that each of them can be located in corresponding position of original image. Second, one CNN takes an image (of any size) as input and outputs a set of feature maps for the image. Afterwards, the extracted feature maps are input to Region Proposal Network (RPN), which may be most likely the areas of wheat mites and output a set of rectangular objective proposals, each with an object score. Then one 256-d vector is generated from the obtained proposals by the other CNN. The vector is input into two fully connected layers, a box-regression layer and a box classification layer, which output the probability scores of the position information and the population of wheat mites, respectively. Moreover, the superposition of the results for the small images is taken as the number of wheat mites for each original image. By using different backbone deep learning networks, ZFnet with five layers and VGG16 with sixteen layers achieved the accuracies of 94.6% and 96.4%, respectively. (c) 2020 Elsevier B.V. All rights reserved. |
| 资助项目 | Open Research Fund of National Engineering Research Center for AgroEcological Big Data Analysis & Application, Anhui University[AE201906] ; National Natural Science Foundation of China[62072002] ; National Natural Science Foundation of China[61672035] ; National Natural Science Foundation of China[U19A2064] ; Educational Commission of Anhui Province[KJ2019ZD05] ; Anhui Province Funds for Excellent Youth Scholars in Colleges[gxyqZD2016068] ; fund of CoInnovation Center for Information Supply & Assurance Technology in AHU[ADXXBZ201705] ; Anhui Scientific Research Foundation for Returness |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000634377200003 |
| 出版者 | ELSEVIER |
| 资助机构 | Open Research Fund of National Engineering Research Center for AgroEcological Big Data Analysis & Application, Anhui University ; National Natural Science Foundation of China ; Educational Commission of Anhui Province ; Anhui Province Funds for Excellent Youth Scholars in Colleges ; fund of CoInnovation Center for Information Supply & Assurance Technology in AHU ; Anhui Scientific Research Foundation for Returness |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/121494]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Ma, Chun; Wang, Bing; Liang, Dong |
| 作者单位 | 1.Anhui Univ Chinese Med, Dept Med Informat Engn, Hefei 230012, Anhui, Peoples R China 2.Anhui Univ, Natl Engn Res Ctr Agroecol Big Data Anal & Applic, Sch Internet, Hefei 230601, Anhui, Peoples R China 3.Anhui Univ, Inst Phys Sci, Hefei 230601, Anhui, Peoples R China 4.Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, Anhui, Peoples R China 5.Anhui Univ, Inst Informat Technol, Hefei 230601, Anhui, Peoples R China 6.Anhui Univ, Sch Elect Engn & Automat, Hefei 230601, Anhui, Peoples R China 7.Anhui Univ Technol, Sch Elect & Informat Engn, Maanshan 243032, Anhui, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Peng,Li, WeiLu,Yao, SiJie,et al. Recognition and counting of wheat mites in wheat fields by a three-step deep learning method[J]. NEUROCOMPUTING,2021,437. |
| APA | Chen, Peng.,Li, WeiLu.,Yao, SiJie.,Ma, Chun.,Zhang, Jun.,...&Liang, Dong.(2021).Recognition and counting of wheat mites in wheat fields by a three-step deep learning method.NEUROCOMPUTING,437. |
| MLA | Chen, Peng,et al."Recognition and counting of wheat mites in wheat fields by a three-step deep learning method".NEUROCOMPUTING 437(2021). |
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。