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Identification of Soybean Varieties Using Hyperspectral Imaging Coupled with Convolutional Neural Network
Soybean variety is connected to stress resistance ability, as well as nutritional and commercial value. Near-infrared hyperspectral imaging was applied to classify three varieties of soybeans (Zhonghuang37, Zhonghuang41, and Zhonghuang55). Pixel-wise spectra were extracted and preprocessed, and aver...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6807262/ https://www.ncbi.nlm.nih.gov/pubmed/31547118 http://dx.doi.org/10.3390/s19194065 |
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author | Zhu, Susu Zhou, Lei Zhang, Chu Bao, Yidan Wu, Baohua Chu, Hangjian Yu, Yue He, Yong Feng, Lei |
author_facet | Zhu, Susu Zhou, Lei Zhang, Chu Bao, Yidan Wu, Baohua Chu, Hangjian Yu, Yue He, Yong Feng, Lei |
author_sort | Zhu, Susu |
collection | PubMed |
description | Soybean variety is connected to stress resistance ability, as well as nutritional and commercial value. Near-infrared hyperspectral imaging was applied to classify three varieties of soybeans (Zhonghuang37, Zhonghuang41, and Zhonghuang55). Pixel-wise spectra were extracted and preprocessed, and average spectra were also obtained. Convolutional neural networks (CNN) using the average spectra and pixel-wise spectra of different numbers of soybeans were built. Pixel-wise CNN models obtained good performance predicting pixel-wise spectra and average spectra. With the increase of soybean numbers, performances were improved, with the classification accuracy of each variety over 90%. Traditionally, the number of samples used for modeling is large. It is time-consuming and requires labor to obtain hyperspectral data from large batches of samples. To explore the possibility of achieving decent identification results with few samples, a majority vote was also applied to the pixel-wise CNN models to identify a single soybean variety. Prediction maps were obtained to present the classification results intuitively. Models using pixel-wise spectra of 60 soybeans showed equivalent performance to those using the average spectra of 810 soybeans, illustrating the possibility of discriminating soybean varieties using few samples by acquiring pixel-wise spectra. |
format | Online Article Text |
id | pubmed-6807262 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-68072622019-11-07 Identification of Soybean Varieties Using Hyperspectral Imaging Coupled with Convolutional Neural Network Zhu, Susu Zhou, Lei Zhang, Chu Bao, Yidan Wu, Baohua Chu, Hangjian Yu, Yue He, Yong Feng, Lei Sensors (Basel) Article Soybean variety is connected to stress resistance ability, as well as nutritional and commercial value. Near-infrared hyperspectral imaging was applied to classify three varieties of soybeans (Zhonghuang37, Zhonghuang41, and Zhonghuang55). Pixel-wise spectra were extracted and preprocessed, and average spectra were also obtained. Convolutional neural networks (CNN) using the average spectra and pixel-wise spectra of different numbers of soybeans were built. Pixel-wise CNN models obtained good performance predicting pixel-wise spectra and average spectra. With the increase of soybean numbers, performances were improved, with the classification accuracy of each variety over 90%. Traditionally, the number of samples used for modeling is large. It is time-consuming and requires labor to obtain hyperspectral data from large batches of samples. To explore the possibility of achieving decent identification results with few samples, a majority vote was also applied to the pixel-wise CNN models to identify a single soybean variety. Prediction maps were obtained to present the classification results intuitively. Models using pixel-wise spectra of 60 soybeans showed equivalent performance to those using the average spectra of 810 soybeans, illustrating the possibility of discriminating soybean varieties using few samples by acquiring pixel-wise spectra. MDPI 2019-09-20 /pmc/articles/PMC6807262/ /pubmed/31547118 http://dx.doi.org/10.3390/s19194065 Text en © 2019 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhu, Susu Zhou, Lei Zhang, Chu Bao, Yidan Wu, Baohua Chu, Hangjian Yu, Yue He, Yong Feng, Lei Identification of Soybean Varieties Using Hyperspectral Imaging Coupled with Convolutional Neural Network |
title | Identification of Soybean Varieties Using Hyperspectral Imaging Coupled with Convolutional Neural Network |
title_full | Identification of Soybean Varieties Using Hyperspectral Imaging Coupled with Convolutional Neural Network |
title_fullStr | Identification of Soybean Varieties Using Hyperspectral Imaging Coupled with Convolutional Neural Network |
title_full_unstemmed | Identification of Soybean Varieties Using Hyperspectral Imaging Coupled with Convolutional Neural Network |
title_short | Identification of Soybean Varieties Using Hyperspectral Imaging Coupled with Convolutional Neural Network |
title_sort | identification of soybean varieties using hyperspectral imaging coupled with convolutional neural network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6807262/ https://www.ncbi.nlm.nih.gov/pubmed/31547118 http://dx.doi.org/10.3390/s19194065 |
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