Comparative study of PCA and LDA for rice seeds quality inspection

Samson Damilola Fabiyi; Hai Vu; Christos Tachtatzis; Paul Murray; David Harle; Trung-Kien Dao; Ivan Andonovic; Jinchang Ren; Stephen Marshall

Comparative study of PCA and LDA for rice seeds quality inspection

Samson Damilola Fabiyi, Hai Vu, Christos Tachtatzis, Paul Murray, David Harle, Trung-Kien Dao, Ivan Andonovic, Jinchang Ren, Stephen Marshall

Research output: Contribution to conference › Paper › peer-review

9 Citations (Scopus)

118 Downloads (Pure)

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.

Original language	English
Number of pages	4
Publication status	Published - 25 Sept 2019
Event	IEEE Africon 2019 - Accra, Ghana Duration: 25 Sept 2019 → 27 Sept 2019 http://africon2019.org/

Conference

Conference	IEEE Africon 2019
Country/Territory	Ghana
City	Accra
Period	25/09/19 → 27/09/19
Internet address	http://africon2019.org/

Keywords

rice seed variety
hyperspectral imaging
PCA
LDA

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Fabiyi-etal-Africon2019-Comparative-study-of-PCA-and-LDA-for-rice-seeds-quality-inspectionAccepted author manuscript, 745 KB

RGB and VIS/NIR Hyperspectral Imaging Data for 90 Rice Seed Varieties
Vu, H. (Creator), Tachtatzis, C. (Creator), Murray, P. (Creator), Harle, D. (Creator), Dao, T.-K. (Creator), Andonovic, I. (Creator), Marshall, S. (Creator) & Fabiyi, S. D. (Creator), Zenodo, 22 Jan 2020
DOI: 10.5281/zenodo.3241922
Dataset

Cite this

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title = "Comparative study of PCA and LDA for rice seeds quality inspection",

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.",

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AU - Fabiyi, Samson Damilola

AU - Vu, Hai

AU - Tachtatzis, Christos

AU - Murray, Paul

AU - Harle, David

AU - Dao, Trung-Kien

AU - Andonovic, Ivan

AU - Ren, Jinchang

AU - Marshall, Stephen

N1 - © 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.

PY - 2019/9/25

Y1 - 2019/9/25

N2 - 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.

AB - 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.

KW - rice seed variety

KW - hyperspectral imaging

KW - PCA

KW - LDA

M3 - Paper

T2 - IEEE Africon 2019

Y2 - 25 September 2019 through 27 September 2019

ER -

Comparative study of PCA and LDA for rice seeds quality inspection

Abstract

Conference

Keywords

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Datasets

RGB and VIS/NIR Hyperspectral Imaging Data for 90 Rice Seed Varieties

Cite this