Ensemble Classification and IoT-Based Pattern Recognition for Crop Disease Monitoring System

Nagasubramanian, G; Sakthivel, RK; Patan, R; Sankayya, M; Daneshmand, M; Gandomi, AH

Ensemble Classification and IoT-Based Pattern Recognition for Crop Disease Monitoring System

Nagasubramanian, G Sakthivel, RK Patan, R Sankayya, M Daneshmand, M Gandomi, AH

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Internet of Things Journal, 2021, 8, (16), pp. 12847-12854
Issue Date:: 2021-08-15

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Field	Value	Language
dc.contributor.author	Nagasubramanian, G
dc.contributor.author	Sakthivel, RK
dc.contributor.author	Patan, R
dc.contributor.author	Sankayya, M
dc.contributor.author	Daneshmand, M
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2021-09-13T22:37:03Z
dc.date.available	2021-09-13T22:37:03Z
dc.date.issued	2021-08-15
dc.identifier.citation	IEEE Internet of Things Journal, 2021, 8, (16), pp. 12847-12854
dc.identifier.issn	2327-4662
dc.identifier.issn	2327-4662
dc.identifier.uri	http://hdl.handle.net/10453/150515
dc.description.abstract	Internet of Things (IoT) in the agriculture field provides crops-oriented data sharing and automatic farming solutions under single network coverage. The components of IoT collect the observable data from different plants at different points. The data gathered through IoT components, such as sensors and cameras, can be used to be manipulated for a better farming-oriented decision-making process. This work proposes a system that observes the crops' growth and leaf diseases continuously for advising farmers in need. To provide analytical statistics on plant growth and disease patterns, the proposed framework uses machine learning (ML) techniques, such as support vector machine (SVM) and convolutional neural network (CNN). This framework produces efficient crop condition notifications to terminal IoT components which are assisting in irrigation, nutrition planning, and environmental compliance related to the farming lands. In this regard, this work proposes ensemble classification and pattern recognition for crop monitoring system (ECPRC) to identify plant diseases at the early stages. The proposed ECPRC uses ensemble nonlinear SVM (ENSVM) for detecting leaf and crop diseases. In addition, this work performs comparative analysis between various ML techniques, such as SVM, CNN, naïve Bayes, and K -nearest neighbors. In this experimental section, the results show that the proposed ECPRC system works optimally compared to the other systems.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Internet of Things Journal
dc.relation.isbasedon	10.1109/JIOT.2021.3072908
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies
dc.title	Ensemble Classification and IoT-Based Pattern Recognition for Crop Disease Monitoring System
dc.type	Journal Article
utslib.citation.volume	8
utslib.for	0805 Distributed Computing
utslib.for	1005 Communications Technologies
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2021-09-13T22:37:01Z
pubs.issue	16
pubs.publication-status	Published
pubs.volume	8
utslib.citation.issue	16

Abstract:

Internet of Things (IoT) in the agriculture field provides crops-oriented data sharing and automatic farming solutions under single network coverage. The components of IoT collect the observable data from different plants at different points. The data gathered through IoT components, such as sensors and cameras, can be used to be manipulated for a better farming-oriented decision-making process. This work proposes a system that observes the crops' growth and leaf diseases continuously for advising farmers in need. To provide analytical statistics on plant growth and disease patterns, the proposed framework uses machine learning (ML) techniques, such as support vector machine (SVM) and convolutional neural network (CNN). This framework produces efficient crop condition notifications to terminal IoT components which are assisting in irrigation, nutrition planning, and environmental compliance related to the farming lands. In this regard, this work proposes ensemble classification and pattern recognition for crop monitoring system (ECPRC) to identify plant diseases at the early stages. The proposed ECPRC uses ensemble nonlinear SVM (ENSVM) for detecting leaf and crop diseases. In addition, this work performs comparative analysis between various ML techniques, such as SVM, CNN, naïve Bayes, and K -nearest neighbors. In this experimental section, the results show that the proposed ECPRC system works optimally compared to the other systems.

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http://hdl.handle.net/10453/150515