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Comparative Evaluation of Remote Sensing Platforms for Almond Yield Prediction

2024Journal article Open access
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dc.contributor.authorGuimarães, Nathalie
dc.contributor.authorFraga, Helder
dc.contributor.authorSousa, Joaquim J.
dc.contributor.authorPádua, Luís
dc.contributor.authorBento, Albino
dc.contributor.authorCouto, Pedro
dc.date.accessioned2024-05-16T15:41:48Z
dc.date.available2024-05-16T15:41:48Z
dc.date.issued2024
dc.description.abstractAlmonds are becoming a central element in the gastronomic and food industry worldwide. Over the last few years, almond production has increased globally. Portugal has become the third most important producer in Europe, where this increasing trend is particularly evident. However, the susceptibility of almond trees to changing climatic conditions presents substantial risks, encompassing yield reduction and quality deterioration. Hence, yield forecasts become crucial for mitigating potential losses and aiding decisionmakers within the agri-food sector. Recent technological advancements and new data analysis techniques have led to the development of more suitable methods to model crop yields. Herein, an innovative approach to predict almond yields in the Tras-os-Montes region of Portugal was developed, by using machine learning regression models (i.e., the random forest regressor, XGBRegressor, gradient boosting regressor, bagging regressor, and AdaBoost regressor), coupled with remote sensing data obtained from different satellite platforms. Satellite data from both proprietary and free platforms at different spatial resolutions were used as features in the study (i.e., the GSMP: 11.13 km, Terra: 1 km, Landsat 8: 30 m, Sentinel-2: 10 m, and PlanetScope: 3 m). The best possible combination of features was analyzed and hyperparameter tuning was applied to enhance the prediction accuracy. Our results suggest that high-resolution data (PlanetScope) combined with irrigation information, vegetation indices, and climate data significantly improves almond yield prediction. The XGBRegressor model performed best when using PlanetScope data, reaching a coefficient of determination (R2) of 0.80. However, alternative options using freely available data with lower spatial resolution, such as GSMaP and Terra MODIS LST, also showed satisfactory performance (R2 = 0.68). This study highlights the potential of integrating machine learning models and remote sensing data for accurate crop yield prediction, providing valuable insights for informed decision support in the almond sector, contributing to the resilience and sustainability of this crop in the face of evolving climate dynamics.pt_PT
dc.description.sponsorshipFinancial support was provided by national funds through FCT-Portuguese Foundation for Science and Technology (UI/BD/150727/2020), under the Doctoral Programme "Agricultural Production Chains-from fork to farm" (PD/00122/2012), and from European Social Funds and the Regional Operational Programme Norte 2020. This study was also supported by CITAB research unit (UIDB/04033/2020; https://doi.org/10.54499/UIDB/04033/2020 (accessed on 17 December2023)), Inov4Agro (LA/P/0126/2020; https://doi.org/10.54499/LA/P/0126/2020 (accessed on17 December 2023)) and by CIMO (UIDB/00690/2020).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationGuimarães, Nathalie; Fraga, Helder; Sousa, Joaquim J.; Pádua, Luís, Bento, Albino; Couto, Pedro (2024). Comparative Evaluation of Remote Sensing Platforms for Almond Yield Prediction. AgriEngineering. EISSN 2624-7402. 6:1, p. 240-258pt_PT
dc.identifier.doi10.3390/agriengineering6010015pt_PT
dc.identifier.eissn2624-7402
dc.identifier.urihttp://hdl.handle.net/10198/29774
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationPD/00122/2012pt_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.subjectPrunus dulcispt_PT
dc.subjectMachine learningpt_PT
dc.subjectRegression modelspt_PT
dc.subjectMultispectral datapt_PT
dc.subjectVegetation indicespt_PT
dc.subjectRemote sensingpt_PT
dc.titleComparative Evaluation of Remote Sensing Platforms for Almond Yield Predictionpt_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.endPage258pt_PT
oaire.citation.issue1pt_PT
oaire.citation.startPage240pt_PT
oaire.citation.titleAgriEngineeringpt_PT
oaire.citation.volume6pt_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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