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Recognition of maize seed varieties based on hyperspectral imaging technology and integrated learning algorithms

Purity is an important factor of maize seed quality that affects yield, and traditional seed purity identification methods are costly or time-consuming. To achieve rapid and accurate detection of the purity of maize seeds, a method for identifying maize seed varieties, using random subspace integrat...

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Autores principales: Yang, Huan, Wang, Cheng, Zhang, Han, Zhou, Ya’nan, Luo, Bin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280578/
https://www.ncbi.nlm.nih.gov/pubmed/37346683
http://dx.doi.org/10.7717/peerj-cs.1354
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author Yang, Huan
Wang, Cheng
Zhang, Han
Zhou, Ya’nan
Luo, Bin
author_facet Yang, Huan
Wang, Cheng
Zhang, Han
Zhou, Ya’nan
Luo, Bin
author_sort Yang, Huan
collection PubMed
description Purity is an important factor of maize seed quality that affects yield, and traditional seed purity identification methods are costly or time-consuming. To achieve rapid and accurate detection of the purity of maize seeds, a method for identifying maize seed varieties, using random subspace integrated learning and hyperspectral imaging technology, was proposed. A hyperspectral image of the maize seed endosperm was collected to obtain a spectral image cube with a wavelength range of 400∼1,000 nm. Methods, including Standard Normal Variate (SNV), multiplicative Scatter Correction (MSC), and Savitzky–Golay First Derivative (SG1) were used to preprocess raw spectral data. Iteratively retains informative variables (IRIV) and competitive adaptive reweighted sampling (CARS) were used to reduce the dimensions of the spectral data. A recognition model of maize seed varieties was established using k-nearest neighbor (KNN), support vector machine (SVM), line discrimination analysis (LDA) and decision tree (DT). Among the preprocessing methods, MSC has the best effect. Among the dimensionality reduction methods, IRIV has the best performance. Among the base classifiers, LDA had the highest precision. To improve the precision in identifying maize seed varieties, LDA was used as the base classifier to establish a random subspace ensemble learning (RSEL) model. Using MSC-IRIV-RSEL, precision increased from 0.9333 to 0.9556, and the Kappa coefficient increased from 0.9174 to 0.9457. This study shows that the method based on hyperspectral imaging technology combined with subspace ensemble learning algorithm is a new method for maize seed purity recognition.
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spelling pubmed-102805782023-06-21 Recognition of maize seed varieties based on hyperspectral imaging technology and integrated learning algorithms Yang, Huan Wang, Cheng Zhang, Han Zhou, Ya’nan Luo, Bin PeerJ Comput Sci Bioinformatics Purity is an important factor of maize seed quality that affects yield, and traditional seed purity identification methods are costly or time-consuming. To achieve rapid and accurate detection of the purity of maize seeds, a method for identifying maize seed varieties, using random subspace integrated learning and hyperspectral imaging technology, was proposed. A hyperspectral image of the maize seed endosperm was collected to obtain a spectral image cube with a wavelength range of 400∼1,000 nm. Methods, including Standard Normal Variate (SNV), multiplicative Scatter Correction (MSC), and Savitzky–Golay First Derivative (SG1) were used to preprocess raw spectral data. Iteratively retains informative variables (IRIV) and competitive adaptive reweighted sampling (CARS) were used to reduce the dimensions of the spectral data. A recognition model of maize seed varieties was established using k-nearest neighbor (KNN), support vector machine (SVM), line discrimination analysis (LDA) and decision tree (DT). Among the preprocessing methods, MSC has the best effect. Among the dimensionality reduction methods, IRIV has the best performance. Among the base classifiers, LDA had the highest precision. To improve the precision in identifying maize seed varieties, LDA was used as the base classifier to establish a random subspace ensemble learning (RSEL) model. Using MSC-IRIV-RSEL, precision increased from 0.9333 to 0.9556, and the Kappa coefficient increased from 0.9174 to 0.9457. This study shows that the method based on hyperspectral imaging technology combined with subspace ensemble learning algorithm is a new method for maize seed purity recognition. PeerJ Inc. 2023-05-10 /pmc/articles/PMC10280578/ /pubmed/37346683 http://dx.doi.org/10.7717/peerj-cs.1354 Text en ©2023 Yang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Yang, Huan
Wang, Cheng
Zhang, Han
Zhou, Ya’nan
Luo, Bin
Recognition of maize seed varieties based on hyperspectral imaging technology and integrated learning algorithms
title Recognition of maize seed varieties based on hyperspectral imaging technology and integrated learning algorithms
title_full Recognition of maize seed varieties based on hyperspectral imaging technology and integrated learning algorithms
title_fullStr Recognition of maize seed varieties based on hyperspectral imaging technology and integrated learning algorithms
title_full_unstemmed Recognition of maize seed varieties based on hyperspectral imaging technology and integrated learning algorithms
title_short Recognition of maize seed varieties based on hyperspectral imaging technology and integrated learning algorithms
title_sort recognition of maize seed varieties based on hyperspectral imaging technology and integrated learning algorithms
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280578/
https://www.ncbi.nlm.nih.gov/pubmed/37346683
http://dx.doi.org/10.7717/peerj-cs.1354
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