A review of neural networks in plant disease detection using hyperspectral data

Golhani, K; Balasundram, SK; Vadamalai, G; Pradhan, B

A review of neural networks in plant disease detection using hyperspectral data

Golhani, K Balasundram, SK Vadamalai, G Pradhan, B

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Publication Type:: Journal Article
Citation:: Information Processing in Agriculture, 2018, 5 (3), pp. 354 - 371
Issue Date:: 2018-09-01

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Field	Value	Language
dc.contributor.author	Golhani, K	en_US
dc.contributor.author	Balasundram, SK	en_US
dc.contributor.author	Vadamalai, G	en_US
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054	en_US
dc.date.issued	2018-09-01	en_US
dc.identifier.citation	Information Processing in Agriculture, 2018, 5 (3), pp. 354 - 371	en_US
dc.identifier.issn	2214-3173	en_US
dc.identifier.uri	http://hdl.handle.net/10453/129318
dc.description.abstract	© 2018 China Agricultural University This paper reviews advanced Neural Network (NN) techniques available to process hyperspectral data, with a special emphasis on plant disease detection. Firstly, we provide a review on NN mechanism, types, models, and classifiers that use different algorithms to process hyperspectral data. Then we highlight the current state of imaging and non-imaging hyperspectral data for early disease detection. The hybridization of NN-hyperspectral approach has emerged as a powerful tool for disease detection and diagnosis. Spectral Disease Index (SDI) is the ratio of different spectral bands of pure disease spectra. Subsequently, we introduce NN techniques for rapid development of SDI. We also highlight current challenges and future trends of hyperspectral data.	en_US
dc.relation.ispartof	Information Processing in Agriculture	en_US
dc.relation.isbasedon	10.1016/j.inpa.2018.05.002	en_US
dc.title	A review of neural networks in plant disease detection using hyperspectral data	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	5	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0607 Plant Biology	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	open_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	5	en_US

Abstract:

© 2018 China Agricultural University This paper reviews advanced Neural Network (NN) techniques available to process hyperspectral data, with a special emphasis on plant disease detection. Firstly, we provide a review on NN mechanism, types, models, and classifiers that use different algorithms to process hyperspectral data. Then we highlight the current state of imaging and non-imaging hyperspectral data for early disease detection. The hybridization of NN-hyperspectral approach has emerged as a powerful tool for disease detection and diagnosis. Spectral Disease Index (SDI) is the ratio of different spectral bands of pure disease spectra. Subsequently, we introduce NN techniques for rapid development of SDI. We also highlight current challenges and future trends of hyperspectral data.

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http://hdl.handle.net/10453/129318