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dc.contributor.authorGómez Aragón, Diego
dc.contributor.authorSalvador González, Pablo 
dc.contributor.authorSanz Justo, María Julia 
dc.contributor.authorCasanova Roque, José Luis 
dc.date.accessioned2022-10-06T12:40:11Z
dc.date.available2022-10-06T12:40:11Z
dc.date.issued2019
dc.identifier.citationRemote Sensing, 2019, vol. 11, n. 15, 1745es
dc.identifier.issn2072-4292es
dc.identifier.urihttps://uvadoc.uva.es/handle/10324/55863
dc.descriptionProducción Científicaes
dc.description.abstractTraditional potato growth models evidence certain limitations, such as the cost of obtaining the input data required to run the models, the lack of spatial information in some instances, or the actual quality of input data. In order to address these issues, we develop a model to predict potato yield using satellite remote sensing. In an effort to offer a good predictive model that improves the state of the art on potato precision agriculture, we use images from the twin Sentinel 2 satellites (European Space Agency—Copernicus Programme) over three growing seasons, applying different machine learning models. First, we fitted nine machine learning algorithms with various pre-processing scenarios using variables from July, August and September based on the red, red-edge and infra-red bands of the spectrum. Second, we selected the best performing models and evaluated them against independent test data. Finally, we repeated the previous two steps using only variables corresponding to July and August. Our results showed that the feature selection step proved vital during data pre-processing in order to reduce multicollinearity among predictors. The Regression Quantile Lasso model (11.67% Root Mean Square Error, RMSE; R2 = 0.88 and 9.18% Mean Absolute Error, MAE) and Leap Backwards model (10.94% RMSE, R2 = 0.89 and 8.95% MAE) performed better when predictors with a correlation coefficient > 0.5 were removed from the dataset. In contrast, the Support Vector Machine Radial (svmRadial) performed better with no feature selection method (11.7% RMSE, R2 = 0.93 and 8.64% MAE). In addition, we used a random forest model to predict potato yields in Castilla y León (Spain) 1–2 months prior to harvest, and obtained satisfactory results (11.16% RMSE, R2 = 0.89 and 8.71% MAE). These results demonstrate the suitability of our models to predict potato yields in the region studied.es
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.publisherMDPIes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subject.classificationMachine learninges
dc.subject.classificationAprendizaje automáticoes
dc.subject.classificationPotato yieldes
dc.subject.classificationPotato yieldes
dc.subject.classificationPatata - Cultivoes
dc.subject.classificationPrecision agriculturees
dc.subject.classificationAgricultura de precisiónes
dc.subject.classificationSatellite remote sensinges
dc.subject.classificationTeledetección satelitales
dc.titlePotato yield prediction using machine learning techniques and Sentinel 2 dataes
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.holder© 2019 The Authorses
dc.identifier.doi10.3390/rs11151745es
dc.relation.publisherversionhttps://www.mdpi.com/2072-4292/11/15/1745es
dc.peerreviewedSIes
dc.rightsAtribución 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones


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