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Combining UAV-Based Multispectral and Thermal Infrared Data with Regression Modeling and SHAP Analysis for Predicting Stomatal Conductance in Almond Orchards

2024Journal article Open access
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dc.contributor.authorGuimarães, Nathalie
dc.contributor.authorSousa, Joaquim J.
dc.contributor.authorCouto, Pedro
dc.contributor.authorBento, Albino
dc.contributor.authorPádua, Luís
dc.date.accessioned2024-07-31T14:43:22Z
dc.date.available2024-07-31T14:43:22Z
dc.date.issued2024
dc.description.abstractUnderstanding and accurately predicting stomatal conductance in almond orchards is critical for effective water-management strategies, especially under challenging climatic conditions. In this study, machine-learning (ML) regression models trained on multispectral (MSP) and thermal infrared (TIR) data acquired from unmanned aerial vehicles (UAVs) are used to address this challenge. Through an analysis of spectral indices calculated from UAV-based data and feature-selection methods, this study investigates the predictive performance of three ML models (extra trees, ET; stochastic gradient descent, SGD; and extreme gradient boosting, XGBoost) in predicting stomatal conductance. The results show that the XGBoost model trained with both MSP and TIR data had the best performance (R2 = 0.87) and highlight the importance of integrating surface-temperature information in addition to other spectral indices to improve prediction accuracy, up to 11% more when compared to the use of only MSP data. Key features, such as the green–red vegetation index, chlorophyll red-edge index, and the ratio between canopy temperature and air temperature (Tc-Ta), prove to be relevant features for model performance and highlight their importance for the assessment of water stress dynamics. Furthermore, the implementation of Shapley additive explanations (SHAP) values facilitates the interpretation of model decisions and provides valuable insights into the contributions of the features. This study contributes to the advancement of precision agriculture by providing a novel approach for stomatal conductance prediction in almond orchards, supporting efforts towards sustainable water management in changing environmental conditions.pt_PT
dc.description.sponsorshipFinancial support was provided by national funds through the FCT\u2014Portuguese Foundation for Science and Technology UI/BD/150727/2020 (https://doi.org/10.54499/UI/BD/150727/2020), under the doctoral program \u201CAgricultural Production Chains\u2014from fork to farm\u201D (PD/00122/2012) and from the European Social Funds and the Regional Operational Programme Norte 2020. This study was also supported by CITAB UIDB/04033/2020 (https://doi.org/10.54499/UIDB/04033/2020), Inov4Agro LA/P/0126/2020 (https://doi.org/10.54499/LA/P/0126/2020), and by CIMO UIDB/00690/2020 (https://doi.org/10.54499/UIDB/00690/2020).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationGuimarães, Nathalie; Sousa, Joaquim J.; Couto, Pedro; Bento, Albino; Pádua, Luís (2024). Combining UAV-Based Multispectral and Thermal Infrared Data with Regression Modeling and SHAP Analysis for Predicting Stomatal Conductance in Almond Orchards. Remote Sensing. ISSN 2072-4292. 16:13, p. 1-19pt_PT
dc.identifier.doi10.3390/rs16132467pt_PT
dc.identifier.issn2072-4292
dc.identifier.urihttp://hdl.handle.net/10198/30140
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationAerial high-resolution imagery to assess almond orchard conditions
dc.relationCentre for the Research and Technology of Agro-Environmental and Biological Sciences
dc.relationInstitute for innovation, capacity building and sustainability of agri-food production
dc.relationMountain Research Center
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectAlmondspt_PT
dc.subjectMachine learningpt_PT
dc.subjectMultispectral datapt_PT
dc.subjectPrecision agriculturept_PT
dc.subjectRemote sensingpt_PT
dc.subjectThermal infrared datapt_PT
dc.subjectVegetation indicespt_PT
dc.titleCombining UAV-Based Multispectral and Thermal Infrared Data with Regression Modeling and SHAP Analysis for Predicting Stomatal Conductance in Almond Orchardspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleAerial high-resolution imagery to assess almond orchard conditions
oaire.awardTitleCentre for the Research and Technology of Agro-Environmental and Biological Sciences
oaire.awardTitleInstitute for innovation, capacity building and sustainability of agri-food production
oaire.awardTitleMountain Research Center
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/POR_NORTE/UI%2FBD%2F150727%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04033%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0126%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00690%2F2020/PT
oaire.citation.endPage19pt_PT
oaire.citation.issue13pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleRemote Sensingpt_PT
oaire.citation.volume16pt_PT
oaire.fundingStreamPOR_NORTE
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
person.familyNameBento
person.givenNameAlbino
person.identifier.ciencia-idD516-325A-9AD7
person.identifier.orcid0000-0001-5215-785X
person.identifier.ridN-9706-2016
person.identifier.scopus-author-id35247694000
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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