An effective automatic system deployed in agricultural Internet of Things using Multi-Context Fusion Network towards crop disease recognition in the wild

doi:10.1016/j.asoc.2020.106128

	An effective automatic system deployed in agricultural Internet of Things using Multi-Context Fusion Network towards crop disease recognition in the wild
	Zhao, Yushan 5; Liu, Liu 1,2,3; Xie, Chengjun 1,2; Wang, Rujing 1,2; Wang, Fangyuan 1,2,3; Bu, Yingqiao 4; Zhang, Shunxiang 5
刊名	APPLIED SOFT COMPUTING
	2020-04-01
卷号	89
关键词	Multiclass crop disease recognition Convolutional Neural Network Internet of Things Multi-Context Fusion Network ContextNet
ISSN号	1568-4946
DOI	10.1016/j.asoc.2020.106128
通讯作者	Liu, Liu(liuliu66@mail.ustc.edu.cn) ; Xie, Chengjun(cjxie@iim.ac.cn)
英文摘要	Automatic crop disease recognition in the wild is a challenging topic in modern intelligent agriculture due to the appearance variances and cluttered background among crop diseases. To overcome these obstacles, the popular methods are to design a Convolutional Neural Network (CNN) model that extracts visual features and identifies crop disease images based on these features. These methods work well on laboratory environment under simple background but achieve low accuracy and poor robustness in processing the raw images captured from practical fields that contain inevitable noises. In this case, Internet of Things (IoT) is attracting increasing attention, with many alternatives to collect high-level contextual information that helps modern recognition system to effectively identify crop diseases in the wild. Motivated by the usefulness of agricultural IoT, a deep learning system using a novel approach named Multi-Context Fusion Network (MCFN), is developed to be deployed in agricultural IoT towards practical crop disease recognition in the wild. Our MCFN firstly adopts a standard CNN backbone to extract highly discriminative and robust visual features from over 50,000 in-field crop disease samples. Next, we exploit contextual features collected from image acquisition sensors as prior information to assist crop disease classification and reduce false positives in our presented ContextNet. Finally, a deep fully connected network is designed to fuse visual features as well as contextual features and output the crop disease prediction. Experimental results on 77 common crop diseases captured in our newly built domain specific dataset show that MCFN with the deep fusion model outperforms the state-of-the-art methods in wild crop disease recognition, and achieves a good identification accuracy of 97.5%. (C) 2020 Elsevier B.V. All rights reserved.
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] ; Major Special Science and Technology Project of Anhui Province, China[201903a06020006]
WOS关键词	CLASSIFICATION ; FEATURES ; MACHINE
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000520042200011
资助机构	National Key Technology R&D Program of China ; National Natural Science Foundation of China ; Major Special Science and Technology Project of Anhui Province, China
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/103485]
专题	中国科学院合肥物质科学研究院
通讯作者	Liu, Liu; Xie, Chengjun
作者单位	1.Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, Peoples R China 2.Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230031, Peoples R China 3.Univ Sci & Technol China, Hefei 230026, Peoples R China 4.Natl Univ Def Technol, Coll Elect Countermeasures, Hefei, Peoples R China 5.Anhui Univ Sci & Technol, Coll Math & Big Data, Huainan 232001, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Yushan,Liu, Liu,Xie, Chengjun,et al. An effective automatic system deployed in agricultural Internet of Things using Multi-Context Fusion Network towards crop disease recognition in the wild[J]. APPLIED SOFT COMPUTING,2020,89.
APA	Zhao, Yushan.,Liu, Liu.,Xie, Chengjun.,Wang, Rujing.,Wang, Fangyuan.,...&Zhang, Shunxiang.(2020).An effective automatic system deployed in agricultural Internet of Things using Multi-Context Fusion Network towards crop disease recognition in the wild.APPLIED SOFT COMPUTING,89.
MLA	Zhao, Yushan,et al."An effective automatic system deployed in agricultural Internet of Things using Multi-Context Fusion Network towards crop disease recognition in the wild".APPLIED SOFT COMPUTING 89(2020).