Comparative study of PCA and LDA for rice seeds quality inspection

Fabiyi, Samson Damilola and Vu, Hai and Tachtatzis, Christos and Murray, Paul and Harle, David and Dao, Trung-Kien and Andonovic, Ivan and Ren, Jinchang and Marshall, Stephen (2019) Comparative study of PCA and LDA for rice seeds quality inspection. In: IEEE Africon 2019, 2019-09-25 - 2019-09-27.

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Abstract

Contamination of rice seeds affects the crop quality, yield and price. Inspection of rice seeds for purity is a very important step for quality assessment. Promising results have been achieved using hyperspectral imaging (HSI) for classification of rice seeds. However, the relatively high number of spectral features in HSI data continues to pose problems during classification which necessitates the use of techniques like Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for dimensionality reduction and feature extraction. This paper presents a comparative study of LDA and PCA as dimensionality reduction techniques for classification of rice seeds using hyperspectral imaging. The results of LDA and PCA on spectral features extracted from hyperspectral images were used for classification using a Random Forest (RF) classifier. Classification results shows that LDA is a superior dimensionality reduction technique to PCA for quality inspection of rice seeds using hyperspectral imaging.

  • Item type:Conference or Workshop Item(Paper)
    ID code:69874
    Dates:
    Date
    Event
    25 September 2019
    Published
    20 July 2019
    Accepted
    Notes:© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:20 Sep 2019 12:45
    Last modified:22 Apr 2024 01:09
    URI:https://strathprints.strath.ac.uk/id/eprint/69874
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