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Identification of Defective Maize Seeds Using Hyperspectral Imaging Combined with Deep Learning

Seed quality affects crop yield and the quality of agricultural products, and traditional identification methods are time-consuming, complex, and irreversibly destructive. This study aims to establish a fast, non-destructive, and effective approach for defect detection in maize seeds based on hypers...

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Autores principales: Xu, Peng, Sun, Wenbin, Xu, Kang, Zhang, Yunpeng, Tan, Qian, Qing, Yiren, Yang, Ranbing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9818215/
https://www.ncbi.nlm.nih.gov/pubmed/36613360
http://dx.doi.org/10.3390/foods12010144
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author Xu, Peng
Sun, Wenbin
Xu, Kang
Zhang, Yunpeng
Tan, Qian
Qing, Yiren
Yang, Ranbing
author_facet Xu, Peng
Sun, Wenbin
Xu, Kang
Zhang, Yunpeng
Tan, Qian
Qing, Yiren
Yang, Ranbing
author_sort Xu, Peng
collection PubMed
description Seed quality affects crop yield and the quality of agricultural products, and traditional identification methods are time-consuming, complex, and irreversibly destructive. This study aims to establish a fast, non-destructive, and effective approach for defect detection in maize seeds based on hyperspectral imaging (HSI) technology combined with deep learning. Raw spectra collected from maize seeds (200 each healthy and worm-eaten) were pre-processed using detrending (DE) and multiple scattering correction (MSC) to highlight the spectral differences between samples. A convolutional neural network architecture (CNN-FES) based on a feature selection mechanism was proposed according to the importance of wavelength in the target classification task. The results show that the subset of 24 feature wavelengths selected by the proposed CNN-FES can capture important feature information in the spectral data more effectively than the conventional successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) algorithms. In addition, a convolutional neural network architecture (CNN-ATM) based on an attentional classification mechanism was designed for one-dimensional spectral data classification and compared with three commonly used machine learning methods, linear discriminant analysis (LDA), random forest (RF), and support vector machine (SVM). The results show that the classification performance of the designed CNN-ATM on the full wavelength does not differ much from the above three methods, and the classification accuracy is above 90% on both the training and test sets. Meanwhile, the accuracy, sensitivity, and specificity of CNN-ATM based on feature wavelength modeling can reach up to 97.50%, 98.28%, and 96.77% at the highest, respectively. The study shows that hyperspectral imaging-based defect detection of maize seed is feasible and effective, and the proposed method has great potential for the processing and analysis of complex hyperspectral data.
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spelling pubmed-98182152023-01-07 Identification of Defective Maize Seeds Using Hyperspectral Imaging Combined with Deep Learning Xu, Peng Sun, Wenbin Xu, Kang Zhang, Yunpeng Tan, Qian Qing, Yiren Yang, Ranbing Foods Article Seed quality affects crop yield and the quality of agricultural products, and traditional identification methods are time-consuming, complex, and irreversibly destructive. This study aims to establish a fast, non-destructive, and effective approach for defect detection in maize seeds based on hyperspectral imaging (HSI) technology combined with deep learning. Raw spectra collected from maize seeds (200 each healthy and worm-eaten) were pre-processed using detrending (DE) and multiple scattering correction (MSC) to highlight the spectral differences between samples. A convolutional neural network architecture (CNN-FES) based on a feature selection mechanism was proposed according to the importance of wavelength in the target classification task. The results show that the subset of 24 feature wavelengths selected by the proposed CNN-FES can capture important feature information in the spectral data more effectively than the conventional successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) algorithms. In addition, a convolutional neural network architecture (CNN-ATM) based on an attentional classification mechanism was designed for one-dimensional spectral data classification and compared with three commonly used machine learning methods, linear discriminant analysis (LDA), random forest (RF), and support vector machine (SVM). The results show that the classification performance of the designed CNN-ATM on the full wavelength does not differ much from the above three methods, and the classification accuracy is above 90% on both the training and test sets. Meanwhile, the accuracy, sensitivity, and specificity of CNN-ATM based on feature wavelength modeling can reach up to 97.50%, 98.28%, and 96.77% at the highest, respectively. The study shows that hyperspectral imaging-based defect detection of maize seed is feasible and effective, and the proposed method has great potential for the processing and analysis of complex hyperspectral data. MDPI 2022-12-27 /pmc/articles/PMC9818215/ /pubmed/36613360 http://dx.doi.org/10.3390/foods12010144 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Xu, Peng
Sun, Wenbin
Xu, Kang
Zhang, Yunpeng
Tan, Qian
Qing, Yiren
Yang, Ranbing
Identification of Defective Maize Seeds Using Hyperspectral Imaging Combined with Deep Learning
title Identification of Defective Maize Seeds Using Hyperspectral Imaging Combined with Deep Learning
title_full Identification of Defective Maize Seeds Using Hyperspectral Imaging Combined with Deep Learning
title_fullStr Identification of Defective Maize Seeds Using Hyperspectral Imaging Combined with Deep Learning
title_full_unstemmed Identification of Defective Maize Seeds Using Hyperspectral Imaging Combined with Deep Learning
title_short Identification of Defective Maize Seeds Using Hyperspectral Imaging Combined with Deep Learning
title_sort identification of defective maize seeds using hyperspectral imaging combined with deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9818215/
https://www.ncbi.nlm.nih.gov/pubmed/36613360
http://dx.doi.org/10.3390/foods12010144
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