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

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 hi...

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Main Authors: Balasundram, Siva Kumar, Golhani, Kamlesh, Vadamalai, Ganesan, Pradhan, Biswajeet
Format: Article
Language:English
Published: Elsevier 2018
Online Access:http://psasir.upm.edu.my/id/eprint/72968/
http://psasir.upm.edu.my/id/eprint/72968/1/PLANT.pdf
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author Balasundram, Siva Kumar
Golhani, Kamlesh
Vadamalai, Ganesan
Pradhan, Biswajeet
author_facet Balasundram, Siva Kumar
Golhani, Kamlesh
Vadamalai, Ganesan
Pradhan, Biswajeet
author_sort Balasundram, Siva Kumar
building UPM Institutional Repository
collection Online Access
description 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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institution Universiti Putra Malaysia
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spelling upm-729682020-11-27T20:13:22Z http://psasir.upm.edu.my/id/eprint/72968/ A review of neural networks in plant disease detection using hyperspectral data Balasundram, Siva Kumar Golhani, Kamlesh Vadamalai, Ganesan Pradhan, Biswajeet 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. Elsevier 2018 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/72968/1/PLANT.pdf Balasundram, Siva Kumar and Golhani, Kamlesh and Vadamalai, Ganesan and Pradhan, Biswajeet (2018) A review of neural networks in plant disease detection using hyperspectral data. Information Processing in Agriculture, 5 (3). 354 - 371. ISSN 2214-3173 https://www.sciencedirect.com/science/article/pii/S2214317317301774 10.1016/j.inpa.2018.05.002
spellingShingle Balasundram, Siva Kumar
Golhani, Kamlesh
Vadamalai, Ganesan
Pradhan, Biswajeet
A review of neural networks in plant disease detection using hyperspectral data
title A review of neural networks in plant disease detection using hyperspectral data
title_full A review of neural networks in plant disease detection using hyperspectral data
title_fullStr A review of neural networks in plant disease detection using hyperspectral data
title_full_unstemmed A review of neural networks in plant disease detection using hyperspectral data
title_short A review of neural networks in plant disease detection using hyperspectral data
title_sort review of neural networks in plant disease detection using hyperspectral data
url http://psasir.upm.edu.my/id/eprint/72968/
http://psasir.upm.edu.my/id/eprint/72968/
http://psasir.upm.edu.my/id/eprint/72968/
http://psasir.upm.edu.my/id/eprint/72968/1/PLANT.pdf