Prediction of peanut seed vigor based on hyperspectral images
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Food Science and Technology (Campinas) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612022000101172 |
Resumo: | Abstract Prediction of seed vigor based on hyperspectral peant. The traditional method is time-consuming and laborious to detect seed vigor. At the same time, the accuracy of the detection result is not high, and it will cause damage to the seed itself. Therefore, in order to achieve rapid and non-destructive detection of peanut seed vigor, the test was performed with original health, artificial aging for 24h and Peanut seeds with different vigor gradients at 72 hours were used as the research samples. Hyperspectral images with a wavelength range of 387~1035 nm were collected, and the image of the central part of the peanut seeds with a pixel size of 60 × 60 after correction was intercepted and the average reflectance value was calculated. After a combination of processing analysis, characteristic band processing, and model selection, a hyperspectral prediction system with the highest correlation to the viability of extracted peanut seeds was finally established. Experiments shown that the combination of hyperspectral imaging technology and the MF-LightGBM-RF model had the best performance, with a prediction accuracy of 92.59% and a fitting time of 1.77s, which simplifies the model and improves efficiency. |
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Food Science and Technology (Campinas) |
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Prediction of peanut seed vigor based on hyperspectral imageshyperspectralpredictive modelingseed viabilitynon-destructive testing techniquesAbstract Prediction of seed vigor based on hyperspectral peant. The traditional method is time-consuming and laborious to detect seed vigor. At the same time, the accuracy of the detection result is not high, and it will cause damage to the seed itself. Therefore, in order to achieve rapid and non-destructive detection of peanut seed vigor, the test was performed with original health, artificial aging for 24h and Peanut seeds with different vigor gradients at 72 hours were used as the research samples. Hyperspectral images with a wavelength range of 387~1035 nm were collected, and the image of the central part of the peanut seeds with a pixel size of 60 × 60 after correction was intercepted and the average reflectance value was calculated. After a combination of processing analysis, characteristic band processing, and model selection, a hyperspectral prediction system with the highest correlation to the viability of extracted peanut seeds was finally established. Experiments shown that the combination of hyperspectral imaging technology and the MF-LightGBM-RF model had the best performance, with a prediction accuracy of 92.59% and a fitting time of 1.77s, which simplifies the model and improves efficiency.Sociedade Brasileira de Ciência e Tecnologia de Alimentos2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612022000101172Food Science and Technology v.42 2022reponame:Food Science and Technology (Campinas)instname:Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA)instacron:SBCTA10.1590/fst.32822info:eu-repo/semantics/openAccessZOU,ZhiyongCHEN,JieZHOU,ManZHAO,YongpengLONG,TaoWU,QingsongXU,Lijiaeng2022-05-31T00:00:00Zoai:scielo:S0101-20612022000101172Revistahttp://www.scielo.br/ctaONGhttps://old.scielo.br/oai/scielo-oai.php||revista@sbcta.org.br1678-457X0101-2061opendoar:2022-05-31T00:00Food Science and Technology (Campinas) - Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA)false |
dc.title.none.fl_str_mv |
Prediction of peanut seed vigor based on hyperspectral images |
title |
Prediction of peanut seed vigor based on hyperspectral images |
spellingShingle |
Prediction of peanut seed vigor based on hyperspectral images ZOU,Zhiyong hyperspectral predictive modeling seed viability non-destructive testing techniques |
title_short |
Prediction of peanut seed vigor based on hyperspectral images |
title_full |
Prediction of peanut seed vigor based on hyperspectral images |
title_fullStr |
Prediction of peanut seed vigor based on hyperspectral images |
title_full_unstemmed |
Prediction of peanut seed vigor based on hyperspectral images |
title_sort |
Prediction of peanut seed vigor based on hyperspectral images |
author |
ZOU,Zhiyong |
author_facet |
ZOU,Zhiyong CHEN,Jie ZHOU,Man ZHAO,Yongpeng LONG,Tao WU,Qingsong XU,Lijia |
author_role |
author |
author2 |
CHEN,Jie ZHOU,Man ZHAO,Yongpeng LONG,Tao WU,Qingsong XU,Lijia |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
ZOU,Zhiyong CHEN,Jie ZHOU,Man ZHAO,Yongpeng LONG,Tao WU,Qingsong XU,Lijia |
dc.subject.por.fl_str_mv |
hyperspectral predictive modeling seed viability non-destructive testing techniques |
topic |
hyperspectral predictive modeling seed viability non-destructive testing techniques |
description |
Abstract Prediction of seed vigor based on hyperspectral peant. The traditional method is time-consuming and laborious to detect seed vigor. At the same time, the accuracy of the detection result is not high, and it will cause damage to the seed itself. Therefore, in order to achieve rapid and non-destructive detection of peanut seed vigor, the test was performed with original health, artificial aging for 24h and Peanut seeds with different vigor gradients at 72 hours were used as the research samples. Hyperspectral images with a wavelength range of 387~1035 nm were collected, and the image of the central part of the peanut seeds with a pixel size of 60 × 60 after correction was intercepted and the average reflectance value was calculated. After a combination of processing analysis, characteristic band processing, and model selection, a hyperspectral prediction system with the highest correlation to the viability of extracted peanut seeds was finally established. Experiments shown that the combination of hyperspectral imaging technology and the MF-LightGBM-RF model had the best performance, with a prediction accuracy of 92.59% and a fitting time of 1.77s, which simplifies the model and improves efficiency. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612022000101172 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612022000101172 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/fst.32822 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Ciência e Tecnologia de Alimentos |
publisher.none.fl_str_mv |
Sociedade Brasileira de Ciência e Tecnologia de Alimentos |
dc.source.none.fl_str_mv |
Food Science and Technology v.42 2022 reponame:Food Science and Technology (Campinas) instname:Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA) instacron:SBCTA |
instname_str |
Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA) |
instacron_str |
SBCTA |
institution |
SBCTA |
reponame_str |
Food Science and Technology (Campinas) |
collection |
Food Science and Technology (Campinas) |
repository.name.fl_str_mv |
Food Science and Technology (Campinas) - Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA) |
repository.mail.fl_str_mv |
||revista@sbcta.org.br |
_version_ |
1752126334621974528 |