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A Classification Method for Seed Viability Assessment with Infrared Thermography
This paper presents a viability assessment method for Pisum sativum L. seeds based on the infrared thermography technique. In this work, different artificial treatments were conducted to prepare seeds samples with different viability. Thermal images and visible images were recorded every five minute...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5424722/ https://www.ncbi.nlm.nih.gov/pubmed/28417907 http://dx.doi.org/10.3390/s17040845 |
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author | Men, Sen Yan, Lei Liu, Jiaxin Qian, Hua Luo, Qinjuan |
author_facet | Men, Sen Yan, Lei Liu, Jiaxin Qian, Hua Luo, Qinjuan |
author_sort | Men, Sen |
collection | PubMed |
description | This paper presents a viability assessment method for Pisum sativum L. seeds based on the infrared thermography technique. In this work, different artificial treatments were conducted to prepare seeds samples with different viability. Thermal images and visible images were recorded every five minutes during the standard five day germination test. After the test, the root length of each sample was measured, which can be used as the viability index of that seed. Each individual seed area in the visible images was segmented with an edge detection method, and the average temperature of the corresponding area in the infrared images was calculated as the representative temperature for this seed at that time. The temperature curve of each seed during germination was plotted. Thirteen characteristic parameters extracted from the temperature curve were analyzed to show the difference of the temperature fluctuations between the seeds samples with different viability. With above parameters, support vector machine (SVM) was used to classify the seed samples into three categories: viable, aged and dead according to the root length, the classification accuracy rate was 95%. On this basis, with the temperature data of only the first three hours during the germination, another SVM model was proposed to classify the seed samples, and the accuracy rate was about 91.67%. From these experimental results, it can be seen that infrared thermography can be applied for the prediction of seed viability, based on the SVM algorithm. |
format | Online Article Text |
id | pubmed-5424722 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-54247222017-05-12 A Classification Method for Seed Viability Assessment with Infrared Thermography Men, Sen Yan, Lei Liu, Jiaxin Qian, Hua Luo, Qinjuan Sensors (Basel) Article This paper presents a viability assessment method for Pisum sativum L. seeds based on the infrared thermography technique. In this work, different artificial treatments were conducted to prepare seeds samples with different viability. Thermal images and visible images were recorded every five minutes during the standard five day germination test. After the test, the root length of each sample was measured, which can be used as the viability index of that seed. Each individual seed area in the visible images was segmented with an edge detection method, and the average temperature of the corresponding area in the infrared images was calculated as the representative temperature for this seed at that time. The temperature curve of each seed during germination was plotted. Thirteen characteristic parameters extracted from the temperature curve were analyzed to show the difference of the temperature fluctuations between the seeds samples with different viability. With above parameters, support vector machine (SVM) was used to classify the seed samples into three categories: viable, aged and dead according to the root length, the classification accuracy rate was 95%. On this basis, with the temperature data of only the first three hours during the germination, another SVM model was proposed to classify the seed samples, and the accuracy rate was about 91.67%. From these experimental results, it can be seen that infrared thermography can be applied for the prediction of seed viability, based on the SVM algorithm. MDPI 2017-04-12 /pmc/articles/PMC5424722/ /pubmed/28417907 http://dx.doi.org/10.3390/s17040845 Text en © 2017 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 Men, Sen Yan, Lei Liu, Jiaxin Qian, Hua Luo, Qinjuan A Classification Method for Seed Viability Assessment with Infrared Thermography |
title | A Classification Method for Seed Viability Assessment with Infrared Thermography |
title_full | A Classification Method for Seed Viability Assessment with Infrared Thermography |
title_fullStr | A Classification Method for Seed Viability Assessment with Infrared Thermography |
title_full_unstemmed | A Classification Method for Seed Viability Assessment with Infrared Thermography |
title_short | A Classification Method for Seed Viability Assessment with Infrared Thermography |
title_sort | classification method for seed viability assessment with infrared thermography |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5424722/ https://www.ncbi.nlm.nih.gov/pubmed/28417907 http://dx.doi.org/10.3390/s17040845 |
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