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Estimation of soluble solids content and fruit temperature in 'rocha' pear using Vis-NIR spectroscopy and the spectraNet–32 deep learning architecture

2023Journal article Open access
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dc.contributor.authorMartins, J. A
dc.contributor.authorRodrigues, Daniela
dc.contributor.authorCavaco, A. M.
dc.contributor.authorAntunes, Maria Dulce
dc.contributor.authorGuerra, Rui Manuel Farinha das Neves
dc.date.accessioned2023-06-21T10:30:44Z
dc.date.available2023-06-21T10:30:44Z
dc.date.issued2023
dc.description.abstractSpectra-based methods are becoming increasingly important in Precision Agriculture as they offer non-destructive, quick tools for measuring the quality of produce. This study introduces a novel approach for esti-mating the soluble solids content (SSC) of 'Rocha' pears using the SpectraNet-32 deep learning architecture, which operates on 1D fruit spectra in the visible to near-infrared region (Vis-NIRS). This method was also able to estimate fruit temperatures, which improved the SSC prediction performance. The dataset consisted of 3300 spectra from 1650 'Rocha' pears collected from local markets over several weeks during the 2010 and 2011 seasons, which had varying edaphoclimatic conditions. Two types of partial least squares (PLS) feature selection methods, under various configurations, were applied to the input spectra to identify the most significant wavelengths for training SpectraNet-32. The model's robustness was also compared to a similar state-of-the-art deep learning architecture, DeepSpectra, as well as four other classical machine learning algorithms: PLS, multiple linear regression (MLR), support vector machine (SVM), and multi-layer perceptron (MLP). In total, 23 different experimental method configurations were assessed, with 150 neural networks each. SpectraNet-32 consistently outperformed other methods in several metrics. On average, it was 6.1% better than PLS in terms of the root mean square error of prediction (RMSEP, 1.08 vs. 1.15%), 7.7% better in prediction gain (PG, 1.67 vs. 1.55), 3.6% better in the coefficient of determination (R2, 0.58 vs. 0.56) and 5.8% better in the coefficient of variation (CV%, 8.35 vs. 8.86).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1016/j.postharvbio.2023.112281pt_PT
dc.identifier.issn0925-5214
dc.identifier.urihttp://hdl.handle.net/10400.1/19722
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relationSmart tools for optimizing management in precision agriculture of citrus orchards
dc.relationCenter for Electronics, Optoelectronics and Telecommunications
dc.relationCenter for Electronics, Optoelectronics and Telecommunications
dc.relationNot Available
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectDeep learningpt_PT
dc.subjectResidual networkpt_PT
dc.subjectNear-infraredpt_PT
dc.subjectSpectroscopypt_PT
dc.subjectPear fruitpt_PT
dc.titleEstimation of soluble solids content and fruit temperature in 'rocha' pear using Vis-NIR spectroscopy and the spectraNet–32 deep learning architecturept_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleSmart tools for optimizing management in precision agriculture of citrus orchards
oaire.awardTitleCenter for Electronics, Optoelectronics and Telecommunications
oaire.awardTitleCenter for Electronics, Optoelectronics and Telecommunications
oaire.awardTitleNot Available
oaire.awardURIinfo:eu-repo/grantAgreement/FCT//SFRH%2FBPD%2F101634%2F2014/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00631%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00631%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/DL 57%2F2016/DL 57%2F2016%2FCP1361%2FCT0040/PT
oaire.citation.startPage112281pt_PT
oaire.citation.titlePostharvest Biology and Technologypt_PT
oaire.citation.volume199pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStreamDL 57/2016
person.familyNameRodrigues
person.familyNameCavaco Guerra
person.familyNameAntunes
person.familyNameGuerra
person.givenNameDaniela
person.givenNameAna Margarida
person.givenNameMaria Dulce
person.givenNameRui
person.identifierC-1285-2012
person.identifier177556
person.identifier.ciencia-idC91E-B434-E327
person.identifier.ciencia-idC11B-9B05-217E
person.identifier.ciencia-id3D16-5067-D6BB
person.identifier.orcid0000-0003-3659-099X
person.identifier.orcid0000-0003-2708-5991
person.identifier.orcid0000-0002-8913-6136
person.identifier.orcid0000-0002-8642-5792
person.identifier.ridA-4683-2012
person.identifier.scopus-author-id6602899707
person.identifier.scopus-author-id7102645075
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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