A New Hybridized Dimensionality Reduction Approach Using Genetic Algorithm and Folded Linear Discriminant Analysis Applied to Hyperspectral Imaging for Effective Rice Seed Classification

Fabiyi, S. orcid.org/0000-0001-9571-2964, Murray, P., Zabalza, J. et al. (3 more authors) (2024) A New Hybridized Dimensionality Reduction Approach Using Genetic Algorithm and Folded Linear Discriminant Analysis Applied to Hyperspectral Imaging for Effective Rice Seed Classification. IEEE Transactions on AgriFood Electronics, 2 (1). 151 -164. ISSN 2771-9529

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Item Type: Article
Authors/Creators:
Copyright, Publisher and Additional Information:

This is an author produced version of an article published in IEEE Transactions on AgriFood Electronics, made available under the terms of the Creative Commons Attribution License (CC-BY), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited.

Keywords: Folded Linear Discriminant Analysis (F-LDA), Genetic Algorithm (GA), Hyperspectral Imaging (HSI), rice seed variety
Dates:
  • Published: 21 March 2024
  • Accepted: 2 March 2024
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 20 Mar 2024 14:01
Last Modified: 16 May 2024 13:04
Status: Published
Publisher: IEEE
Identification Number: 10.1109/TAFE.2024.3374753
Open Archives Initiative ID (OAI ID):

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