Modeling Hyperspectral Response of Water-Stress Induced Lettuce Plants Using Artificial Neural Networks

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dc.contributor.authorOsco, Lucas Prado
dc.contributor.authorMarques Ramos, Ana Paula
dc.contributor.authorSaito Moriya, Erika Akemi [UNESP]
dc.contributor.authorBavaresco, Lorrayne Guimaraes
dc.contributor.authorLima, Bruna Coelho de
dc.contributor.authorEstrabis, Nayara
dc.contributor.authorPereira, Danilo Roberto
dc.contributor.authorCreste, Jose Eduardo
dc.contributor.authorMarcato Junior, Jose
dc.contributor.authorGoncalves, Wesley Nunes
dc.contributor.authorImai, Nilton Nobuhiro [UNESP]
dc.contributor.authorLi, Jonathan
dc.contributor.authorLiesenberg, Veraldo
dc.contributor.authorAraujo, Fabio Fernando de
dc.contributor.institutionUniversidade Federal de Mato Grosso do Sul (UFMS)
dc.contributor.institutionUniv Western Sao Paulo
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniv Waterloo
dc.contributor.institutionUniv Estado Santa Catarina
dc.date.accessioned2020-12-10T19:47:16Z
dc.date.available2020-12-10T19:47:16Z
dc.date.issued2019-12-01
dc.description.abstractModeling 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.en
dc.description.affiliationUniv Fed Mato Grosso do Sul, Fac Engn Architecture & Urbanism & Geog, Av Costa & Silva, BR-79070900 Campo Grande, MS, Brazil
dc.description.affiliationUniv Western Sao Paulo, Environm & Reg Dev, R Jose Bongiovani 700, BR-19050920 Presidente Prudente, Brazil
dc.description.affiliationSao Paulo State Univ, Dept Cartog Sci, BR-19060900 Presidente Prudente, Brazil
dc.description.affiliationUniv Western Sao Paulo, Agron Dev, R Jose Bongiovani 700, BR-19050920 Presidente Prudente, Brazil
dc.description.affiliationUniv Waterloo, Dept Geog & Environm Management, Waterloo, ON N2L 3G1, Canada
dc.description.affiliationUniv Waterloo, Dept Syst Design Engn, Waterloo, ON N2L 3G1, Canada
dc.description.affiliationUniv Estado Santa Catarina, Forest Engn Dept, BR-88040900 Florianopolis, SC, Brazil
dc.description.affiliationUnespSao Paulo State Univ, Dept Cartog Sci, BR-19060900 Presidente Prudente, Brazil
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipFAPESC
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipIdCAPES: p: 88881.311850/2018-01
dc.description.sponsorshipIdFAPESP: p: 2013/20328-0
dc.description.sponsorshipIdFAPESC: 2017TR1762
dc.description.sponsorshipIdCNPq: 313887/2018-7
dc.format.extent15
dc.identifierhttp://dx.doi.org/10.3390/rs11232797
dc.identifier.citationRemote Sensing. Basel: Mdpi, v. 11, n. 23, 15 p., 2019.
dc.identifier.doi10.3390/rs11232797
dc.identifier.lattes2985771102505330
dc.identifier.orcid0000-0003-0516-0567
dc.identifier.urihttp://hdl.handle.net/11449/196509
dc.identifier.wosWOS:000508382100078
dc.language.isoeng
dc.publisherMdpi
dc.relation.ispartofRemote Sensing
dc.sourceWeb of Science
dc.subjectspectroscopy
dc.subjectartificial intelligence
dc.subjectproximal sensing data
dc.subjectprecision agriculture
dc.titleModeling Hyperspectral Response of Water-Stress Induced Lettuce Plants Using Artificial Neural Networksen
dc.typeArtigo
dcterms.rightsHolderMdpi
unesp.author.lattes2985771102505330[11]
unesp.author.orcid0000-0002-0258-536X[1]
unesp.author.orcid0000-0001-6633-2903[2]
unesp.author.orcid0000-0001-6590-6733[4]
unesp.author.orcid0000-0003-0564-7818[13]
unesp.author.orcid0000-0003-0516-0567[11]

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