Spectral inversion model of the crushing rate of soybean under mechanized harvesting
Autor(a) principal: | |
---|---|
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=S0101-20612022000101121 |
Resumo: | Abstract Rapid and timely acquisition of the crushing rate can help in assessing the performance of combine harvesters, which is very important for agricultural production. The spectral reflectance of soybean provides an alternative method to the classical physical and chemical analysis of the crushing rate of soybean in laboratory. Therefore, hyperspectral imaging can be used to rapidly obtain the crushing rate of soybean. In this study, the hyperspectral method was employed, and the application of inter-correlation analysis was explored in the optimization and quantitative analysis of hyperspectral bands. The crushing rate of 130 soybean samples collected from a combine harvester was investigated through physical analysis in the laboratory. Subsequently, the raw hyperspectral reflectance of soybean samples was measured using a spectroradiometer equipped with a high intensity contact probe under darkroom conditions. Next, the raw spectral reflectance (REF) and the logarithmic reciprocal pretreatment spectrum data (LR) were analyzed and compared. The effective wavelengths were selected according to the results of the inter-correlation analysis. Regression models of the crushing rate with different indices were established using a least squares support vector machine (LS-SVM). The inversion results of the model were validated and compared with each other. The experimental results show that sensitive bands from REF are 1061, 1068, 1074, 1090, 2085, 2092, 2095, and 2103 nm. Sensitive bands from LR are 677, 1039, 1078, 1093, 1101, 1956, 2088, and 2107 nm. The results showed that REF was the optimal spectral index in the LS-SVM regression model (Rc2 was 0.939, and Rp2 was 0.915). The inter-correlation analysis method could not only support efficient selection of hyperspectral bands, but also retain the original sample information. The REF hyperspectral inversion model based on LS-SVM can realize rapid on-line monitoring of the performance (crushing rate) of grain combine harvesters in the future. |
id |
SBCTA-1_85f180780e1f1c36426eca687ab384ee |
---|---|
oai_identifier_str |
oai:scielo:S0101-20612022000101121 |
network_acronym_str |
SBCTA-1 |
network_name_str |
Food Science and Technology (Campinas) |
repository_id_str |
|
spelling |
Spectral inversion model of the crushing rate of soybean under mechanized harvestingsoybeancrushing ratemechanized operationsleast squares support vector machinehyperspectral remote sensingAbstract Rapid and timely acquisition of the crushing rate can help in assessing the performance of combine harvesters, which is very important for agricultural production. The spectral reflectance of soybean provides an alternative method to the classical physical and chemical analysis of the crushing rate of soybean in laboratory. Therefore, hyperspectral imaging can be used to rapidly obtain the crushing rate of soybean. In this study, the hyperspectral method was employed, and the application of inter-correlation analysis was explored in the optimization and quantitative analysis of hyperspectral bands. The crushing rate of 130 soybean samples collected from a combine harvester was investigated through physical analysis in the laboratory. Subsequently, the raw hyperspectral reflectance of soybean samples was measured using a spectroradiometer equipped with a high intensity contact probe under darkroom conditions. Next, the raw spectral reflectance (REF) and the logarithmic reciprocal pretreatment spectrum data (LR) were analyzed and compared. The effective wavelengths were selected according to the results of the inter-correlation analysis. Regression models of the crushing rate with different indices were established using a least squares support vector machine (LS-SVM). The inversion results of the model were validated and compared with each other. The experimental results show that sensitive bands from REF are 1061, 1068, 1074, 1090, 2085, 2092, 2095, and 2103 nm. Sensitive bands from LR are 677, 1039, 1078, 1093, 1101, 1956, 2088, and 2107 nm. The results showed that REF was the optimal spectral index in the LS-SVM regression model (Rc2 was 0.939, and Rp2 was 0.915). The inter-correlation analysis method could not only support efficient selection of hyperspectral bands, but also retain the original sample information. The REF hyperspectral inversion model based on LS-SVM can realize rapid on-line monitoring of the performance (crushing rate) of grain combine harvesters in the future.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=S0101-20612022000101121Food 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.123221info:eu-repo/semantics/openAccessCHEN,ManNI,YouliangJIN,ChengqianLIU,ZhengXU,Jinshaneng2022-05-03T00:00:00Zoai:scielo:S0101-20612022000101121Revistahttp://www.scielo.br/ctaONGhttps://old.scielo.br/oai/scielo-oai.php||revista@sbcta.org.br1678-457X0101-2061opendoar:2022-05-03T00:00Food Science and Technology (Campinas) - Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA)false |
dc.title.none.fl_str_mv |
Spectral inversion model of the crushing rate of soybean under mechanized harvesting |
title |
Spectral inversion model of the crushing rate of soybean under mechanized harvesting |
spellingShingle |
Spectral inversion model of the crushing rate of soybean under mechanized harvesting CHEN,Man soybean crushing rate mechanized operations least squares support vector machine hyperspectral remote sensing |
title_short |
Spectral inversion model of the crushing rate of soybean under mechanized harvesting |
title_full |
Spectral inversion model of the crushing rate of soybean under mechanized harvesting |
title_fullStr |
Spectral inversion model of the crushing rate of soybean under mechanized harvesting |
title_full_unstemmed |
Spectral inversion model of the crushing rate of soybean under mechanized harvesting |
title_sort |
Spectral inversion model of the crushing rate of soybean under mechanized harvesting |
author |
CHEN,Man |
author_facet |
CHEN,Man NI,Youliang JIN,Chengqian LIU,Zheng XU,Jinshan |
author_role |
author |
author2 |
NI,Youliang JIN,Chengqian LIU,Zheng XU,Jinshan |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
CHEN,Man NI,Youliang JIN,Chengqian LIU,Zheng XU,Jinshan |
dc.subject.por.fl_str_mv |
soybean crushing rate mechanized operations least squares support vector machine hyperspectral remote sensing |
topic |
soybean crushing rate mechanized operations least squares support vector machine hyperspectral remote sensing |
description |
Abstract Rapid and timely acquisition of the crushing rate can help in assessing the performance of combine harvesters, which is very important for agricultural production. The spectral reflectance of soybean provides an alternative method to the classical physical and chemical analysis of the crushing rate of soybean in laboratory. Therefore, hyperspectral imaging can be used to rapidly obtain the crushing rate of soybean. In this study, the hyperspectral method was employed, and the application of inter-correlation analysis was explored in the optimization and quantitative analysis of hyperspectral bands. The crushing rate of 130 soybean samples collected from a combine harvester was investigated through physical analysis in the laboratory. Subsequently, the raw hyperspectral reflectance of soybean samples was measured using a spectroradiometer equipped with a high intensity contact probe under darkroom conditions. Next, the raw spectral reflectance (REF) and the logarithmic reciprocal pretreatment spectrum data (LR) were analyzed and compared. The effective wavelengths were selected according to the results of the inter-correlation analysis. Regression models of the crushing rate with different indices were established using a least squares support vector machine (LS-SVM). The inversion results of the model were validated and compared with each other. The experimental results show that sensitive bands from REF are 1061, 1068, 1074, 1090, 2085, 2092, 2095, and 2103 nm. Sensitive bands from LR are 677, 1039, 1078, 1093, 1101, 1956, 2088, and 2107 nm. The results showed that REF was the optimal spectral index in the LS-SVM regression model (Rc2 was 0.939, and Rp2 was 0.915). The inter-correlation analysis method could not only support efficient selection of hyperspectral bands, but also retain the original sample information. The REF hyperspectral inversion model based on LS-SVM can realize rapid on-line monitoring of the performance (crushing rate) of grain combine harvesters in the future. |
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=S0101-20612022000101121 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612022000101121 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/fst.123221 |
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_ |
1752126334225612800 |