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Evaluation of a deep learning approach for predicting the Fraction of Transpirable Soil Water in vineyards

2023-02-22Journal article Open access
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dc.contributor.authorAlibabaei, Khadijeh
dc.contributor.authorGaspar, Pedro Dinis
dc.contributor.authorCampos, Rebeca M
dc.contributor.authorRodrigues, Gonçalo C.
dc.contributor.authorLopes, Carlos M.
dc.date.accessioned2024-02-02T10:41:33Z
dc.date.available2024-02-02T10:41:33Z
dc.date.issued2023-02-22
dc.description.abstractAs agriculture has an increasing impact on the environment, new techniques can help meet future food needs while maintaining or reducing the environmental footprint. Those techniques must incorporate a range of sensing, communication, and data analysis technologies to make informed management decisions, such as those related to the use of water, fertilizer, pesticides, seeds, fuel, labor, etc., to help increase crop production and reduce water and nutrient losses, as well as negative environmental impacts. In this study, a Bidirectional Long Short-Term Memory (BiLSTM) model was trained on real data from Internet of Things sensors in a vineyard located in the Douro wine-growing region, from 2018–2021, to evaluate the ability of this model to predict the Fraction of Transpirable Soil Water (FTSW). The model uses historical data, including reference evapotranspiration, relative humidity, vapor pressure deficit, and rainfall, and outputs the FTSW for periods of one, three, five, and seven days. The model achieved an RMSE between 8.3% and 16.6% and an 𝑅2-score between 0.75 and 0.93. The model was validated on an independent dataset collected in 2002–2004 from a different vineyard located in the Lisbon wine-growing region, Portugal, and achieved an 𝑅2-score of 87% and an RMSE of 10.36%. Finally, the performance of the FTSW in the vineyard prediction model was compared with that of the Random Forest model, support vector regression, and linear regression. The results showed that BiLSTM performed better than the RF model on the unseen data, and the BiLSTM model can be considered a suitable model for the accurate prediction of the FTSW.pt_PT
dc.description.sponsorshipThe work is supported by by the R&D Project BioDAgro – Sistema operacional inteligente de informação e suporte á decisão em AgroBiodiversidade, project PD20-00011, promoted by Fundação La Caixa and Fundação para a Ciência e a Tecnologia, taking place at the C-MAST - Centre for Mechanical and Aerospace Sciences and Technology, Department of Electromechanical Engineering of the University of Beira Interior, Covilhã, Portugal.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationAlibabaei, K.; Gaspar, P.D.; Campos, R.M.; Rodrigues, G.C.; Lopes, C.M. Evaluation of a Deep Learning Approach for Predicting the Fraction of Transpirable SoilWater in Vineyards. Appl. Sci. 2023, 13, 2815. https://doi.org/10.3390/app13052815pt_PT
dc.identifier.doi10.3390/app13052815pt_PT
dc.identifier.issn2076-3417
dc.identifier.urihttp://hdl.handle.net/10400.6/14224
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherApplied Sciencespt_PT
dc.relationCentre for Mechanical and Aerospace Science and Technologies
dc.relation.publisherversionhttps://www.mdpi.com/2076-3417/13/5/2815pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectAgriculturept_PT
dc.subjectSupport decision-making algorithmspt_PT
dc.subjectDeep learningpt_PT
dc.subjectFTSWpt_PT
dc.subjectBiLSTMpt_PT
dc.subjectLSTMpt_PT
dc.titleEvaluation of a deep learning approach for predicting the Fraction of Transpirable Soil Water in vineyardspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleCentre for Mechanical and Aerospace Science and Technologies
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00151%2F2020/PT
oaire.citation.titleApplied Sciencespt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameAlibabaei
person.familyNameGaspar
person.familyNameMateus Ramos de Campos
person.familyNameRodrigues
person.familyNameLopes
person.givenNameKhadijeh
person.givenNamePedro Dinis
person.givenNameRebeca
person.givenNameGonçalo
person.givenNameCarlos
person.identifier1111633
person.identifier388161
person.identifier.ciencia-id6111-9F05-2916
person.identifier.ciencia-id1E15-9C1A-3737
person.identifier.ciencia-id3A1E-764B-F58C
person.identifier.orcid0000-0002-2319-8211
person.identifier.orcid0000-0003-1691-1709
person.identifier.orcid0000-0003-0672-4408
person.identifier.orcid0000-0002-6189-2079
person.identifier.orcid0000-0003-2456-1200
person.identifier.ridN-3016-2013
person.identifier.ridR-9891-2019
person.identifier.scopus-author-id57419570900
person.identifier.scopus-author-id26659248800
person.identifier.scopus-author-id8082349000
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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