干旱区生态环境知识资源中心(Arid): Hybrid neural network classification for irrigation control in WSN based precision agriculture

Arid

DOI	10.1007/s12652-020-02704-6
	Hybrid neural network classification for irrigation control in WSN based precision agriculture
	Anguraj, Dinesh Kumar; Mandhala, Venkata Naresh; Bhattacharyya, Debnath; Kim, Tai-hoon
通讯作者	Kim, TH (corresponding author), Beijing Jiaotong Univ, Sch Econ & Management, Beijing, Peoples R China.
来源期刊	JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
ISSN	1868-5137
EISSN	1868-5145
出版年	2021-01
英文摘要	Decision support systems (DSS) were built using the support of wireless sensors network (WSN) for resolving many real-world issues. Precision agriculture (PA) is the most popular area which requires DSS. Numerous agricultural cropping schemes in arid and semiarid areas practice irrigation process which is a crucial one and also here the main concern is water applications and management. An automatic Smart data mining based Irrigation Support Scheme is projected in our work in order to manage the irrigation in agriculture. Then for irrigation management, the author introduced the work Convolutional Neural Support Vector Machines Hybrid Classifier (CNSVMHC). This, in turn, avoids the weekly irrigations which is required for plantation. In this proposed research work, real time soil moisture content (MC) data collection were performed with the assistance of WSN and then irrigation will be controlled according to those collected data through CNSVMHC for an efficient irrigation management. The CNSVMHC is a heterogeneous combination of the convolutional neural network (CNN) and support vector machines (SVM), where the output layer of the CNN is substituted by an SVM. A control system with closed loop scheme was enabled through this process, which adjust the decision support scheme to approximation faults and local perturbations. As of the intricate and varied information dependent systems, the effectiveness and consistency of irrigation can be preserved through the soil, weather, and water and crop data. In order to do this process, we need help from the sensor network and other agricultural techniques for storing and using the rain water, maximizing their crop productivity, minimize the cost for cultivation and utilize the real time values rather than depending on prediction.
英文关键词	Wireless sensors network Decision support systems Hybrid neural network Agricultural cropping systems Precision agriculture Automatic smart data mining
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000607997500007
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Telecommunications
WOS研究方向	Computer Science ; Telecommunications
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/367493
作者单位	[Anguraj, Dinesh Kumar; Mandhala, Venkata Naresh; Bhattacharyya, Debnath] Koneru Lakshmaiah Educ Fdn, Dept Comp Sci & Engn, Guntur, Andhra Pradesh, India; [Kim, Tai-hoon] Beijing Jiaotong Univ, Sch Econ & Management, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Anguraj, Dinesh Kumar,Mandhala, Venkata Naresh,Bhattacharyya, Debnath,et al. Hybrid neural network classification for irrigation control in WSN based precision agriculture[J],2021.
APA	Anguraj, Dinesh Kumar,Mandhala, Venkata Naresh,Bhattacharyya, Debnath,&Kim, Tai-hoon.(2021).Hybrid neural network classification for irrigation control in WSN based precision agriculture.JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING.
MLA	Anguraj, Dinesh Kumar,et al."Hybrid neural network classification for irrigation control in WSN based precision agriculture".JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING (2021).