Modeling Hyperspectral Response of Water-Stress Induced Lettuce Plants Using Artificial Neural Networks
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , , , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.3390/rs11232797 http://hdl.handle.net/11449/196509 |
Resumo: | Modeling the hyperspectral response of vegetables is important for estimating water stress through a noninvasive approach. This article evaluates the hyperspectral response of water-stress induced lettuce (Lactuca sativa L.) using artificial neural networks (ANN). We evenly split 36 lettuce pots into three groups: control, stress, and bacteria. Hyperspectral response was measured four times, during 14 days of stress induction, with an ASD Fieldspec HandHeld spectroradiometer (325-1075 nm). Both reflectance and absorbance measurements were calculated. Different biophysical parameters were also evaluated. The performance of the ANN approach was compared against other machine learning algorithms. Our results show that the ANN approach could separate the water-stressed lettuce from the non-stressed group with up to 80% accuracy at the beginning of the experiment. Additionally, this accuracy improved at the end of the experiment, reaching an accuracy of up to 93%. Absorbance data offered better accuracy than reflectance data to model it. This study demonstrated that it is possible to detect early stages of water stress in lettuce plants with high accuracy based on an ANN approach applied to hyperspectral data. The methodology has the potential to be applied to other species and cultivars in agricultural fields. |
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Modeling Hyperspectral Response of Water-Stress Induced Lettuce Plants Using Artificial Neural Networksspectroscopyartificial intelligenceproximal sensing dataprecision agricultureModeling the hyperspectral response of vegetables is important for estimating water stress through a noninvasive approach. This article evaluates the hyperspectral response of water-stress induced lettuce (Lactuca sativa L.) using artificial neural networks (ANN). We evenly split 36 lettuce pots into three groups: control, stress, and bacteria. Hyperspectral response was measured four times, during 14 days of stress induction, with an ASD Fieldspec HandHeld spectroradiometer (325-1075 nm). Both reflectance and absorbance measurements were calculated. Different biophysical parameters were also evaluated. The performance of the ANN approach was compared against other machine learning algorithms. Our results show that the ANN approach could separate the water-stressed lettuce from the non-stressed group with up to 80% accuracy at the beginning of the experiment. Additionally, this accuracy improved at the end of the experiment, reaching an accuracy of up to 93%. Absorbance data offered better accuracy than reflectance data to model it. This study demonstrated that it is possible to detect early stages of water stress in lettuce plants with high accuracy based on an ANN approach applied to hyperspectral data. The methodology has the potential to be applied to other species and cultivars in agricultural fields.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)FAPESCConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Univ Fed Mato Grosso do Sul, Fac Engn Architecture & Urbanism & Geog, Av Costa & Silva, BR-79070900 Campo Grande, MS, BrazilUniv Western Sao Paulo, Environm & Reg Dev, R Jose Bongiovani 700, BR-19050920 Presidente Prudente, BrazilSao Paulo State Univ, Dept Cartog Sci, BR-19060900 Presidente Prudente, BrazilUniv Western Sao Paulo, Agron Dev, R Jose Bongiovani 700, BR-19050920 Presidente Prudente, BrazilUniv Waterloo, Dept Geog & Environm Management, Waterloo, ON N2L 3G1, CanadaUniv Waterloo, Dept Syst Design Engn, Waterloo, ON N2L 3G1, CanadaUniv Estado Santa Catarina, Forest Engn Dept, BR-88040900 Florianopolis, SC, BrazilSao Paulo State Univ, Dept Cartog Sci, BR-19060900 Presidente Prudente, BrazilCAPES: p: 88881.311850/2018-01FAPESP: p: 2013/20328-0FAPESC: 2017TR1762CNPq: 313887/2018-7MdpiUniversidade Federal de Mato Grosso do Sul (UFMS)Univ Western Sao PauloUniversidade Estadual Paulista (Unesp)Univ WaterlooUniv Estado Santa CatarinaOsco, Lucas PradoMarques Ramos, Ana PaulaSaito Moriya, Erika Akemi [UNESP]Bavaresco, Lorrayne GuimaraesLima, Bruna Coelho deEstrabis, NayaraPereira, Danilo RobertoCreste, Jose EduardoMarcato Junior, JoseGoncalves, Wesley NunesImai, Nilton Nobuhiro [UNESP]Li, JonathanLiesenberg, VeraldoAraujo, Fabio Fernando de2020-12-10T19:47:16Z2020-12-10T19:47:16Z2019-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article15http://dx.doi.org/10.3390/rs11232797Remote Sensing. Basel: Mdpi, v. 11, n. 23, 15 p., 2019.http://hdl.handle.net/11449/19650910.3390/rs11232797WOS:00050838210007829857711025053300000-0003-0516-0567Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengRemote Sensinginfo:eu-repo/semantics/openAccess2024-06-18T15:02:06Zoai:repositorio.unesp.br:11449/196509Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T23:14:42.384005Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Modeling Hyperspectral Response of Water-Stress Induced Lettuce Plants Using Artificial Neural Networks |
title |
Modeling Hyperspectral Response of Water-Stress Induced Lettuce Plants Using Artificial Neural Networks |
spellingShingle |
Modeling Hyperspectral Response of Water-Stress Induced Lettuce Plants Using Artificial Neural Networks Osco, Lucas Prado spectroscopy artificial intelligence proximal sensing data precision agriculture |
title_short |
Modeling Hyperspectral Response of Water-Stress Induced Lettuce Plants Using Artificial Neural Networks |
title_full |
Modeling Hyperspectral Response of Water-Stress Induced Lettuce Plants Using Artificial Neural Networks |
title_fullStr |
Modeling Hyperspectral Response of Water-Stress Induced Lettuce Plants Using Artificial Neural Networks |
title_full_unstemmed |
Modeling Hyperspectral Response of Water-Stress Induced Lettuce Plants Using Artificial Neural Networks |
title_sort |
Modeling Hyperspectral Response of Water-Stress Induced Lettuce Plants Using Artificial Neural Networks |
author |
Osco, Lucas Prado |
author_facet |
Osco, Lucas Prado Marques Ramos, Ana Paula Saito Moriya, Erika Akemi [UNESP] Bavaresco, Lorrayne Guimaraes Lima, Bruna Coelho de Estrabis, Nayara Pereira, Danilo Roberto Creste, Jose Eduardo Marcato Junior, Jose Goncalves, Wesley Nunes Imai, Nilton Nobuhiro [UNESP] Li, Jonathan Liesenberg, Veraldo Araujo, Fabio Fernando de |
author_role |
author |
author2 |
Marques Ramos, Ana Paula Saito Moriya, Erika Akemi [UNESP] Bavaresco, Lorrayne Guimaraes Lima, Bruna Coelho de Estrabis, Nayara Pereira, Danilo Roberto Creste, Jose Eduardo Marcato Junior, Jose Goncalves, Wesley Nunes Imai, Nilton Nobuhiro [UNESP] Li, Jonathan Liesenberg, Veraldo Araujo, Fabio Fernando de |
author2_role |
author author author author author author author author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Federal de Mato Grosso do Sul (UFMS) Univ Western Sao Paulo Universidade Estadual Paulista (Unesp) Univ Waterloo Univ Estado Santa Catarina |
dc.contributor.author.fl_str_mv |
Osco, Lucas Prado Marques Ramos, Ana Paula Saito Moriya, Erika Akemi [UNESP] Bavaresco, Lorrayne Guimaraes Lima, Bruna Coelho de Estrabis, Nayara Pereira, Danilo Roberto Creste, Jose Eduardo Marcato Junior, Jose Goncalves, Wesley Nunes Imai, Nilton Nobuhiro [UNESP] Li, Jonathan Liesenberg, Veraldo Araujo, Fabio Fernando de |
dc.subject.por.fl_str_mv |
spectroscopy artificial intelligence proximal sensing data precision agriculture |
topic |
spectroscopy artificial intelligence proximal sensing data precision agriculture |
description |
Modeling the hyperspectral response of vegetables is important for estimating water stress through a noninvasive approach. This article evaluates the hyperspectral response of water-stress induced lettuce (Lactuca sativa L.) using artificial neural networks (ANN). We evenly split 36 lettuce pots into three groups: control, stress, and bacteria. Hyperspectral response was measured four times, during 14 days of stress induction, with an ASD Fieldspec HandHeld spectroradiometer (325-1075 nm). Both reflectance and absorbance measurements were calculated. Different biophysical parameters were also evaluated. The performance of the ANN approach was compared against other machine learning algorithms. Our results show that the ANN approach could separate the water-stressed lettuce from the non-stressed group with up to 80% accuracy at the beginning of the experiment. Additionally, this accuracy improved at the end of the experiment, reaching an accuracy of up to 93%. Absorbance data offered better accuracy than reflectance data to model it. This study demonstrated that it is possible to detect early stages of water stress in lettuce plants with high accuracy based on an ANN approach applied to hyperspectral data. The methodology has the potential to be applied to other species and cultivars in agricultural fields. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-12-01 2020-12-10T19:47:16Z 2020-12-10T19:47:16Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.3390/rs11232797 Remote Sensing. Basel: Mdpi, v. 11, n. 23, 15 p., 2019. http://hdl.handle.net/11449/196509 10.3390/rs11232797 WOS:000508382100078 2985771102505330 0000-0003-0516-0567 |
url |
http://dx.doi.org/10.3390/rs11232797 http://hdl.handle.net/11449/196509 |
identifier_str_mv |
Remote Sensing. Basel: Mdpi, v. 11, n. 23, 15 p., 2019. 10.3390/rs11232797 WOS:000508382100078 2985771102505330 0000-0003-0516-0567 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Remote Sensing |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
15 |
dc.publisher.none.fl_str_mv |
Mdpi |
publisher.none.fl_str_mv |
Mdpi |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129501943562240 |