Potential Use of Data-Driven Models to Estimate and Predict Soybean Yields at National Scale in Brazil
Leonardo Amaral Monteiro, Rafael Martínez Ramos, Rafael Battisti, Johnny Rodrigues Soares, Julianne de Castro Oliveira, Gleyce Kelly Dantas Araújo Figueiredo, Rubens Augusto Camargo Lamparelli, Claas Nendel, Marcos Lana
Topics & Concepts
Random forestSupport vector machineScale (ratio)Yield (engineering)Food securityRobustness (evolution)Predictive modellingStatisticsLinear regressionCrop yieldMissing dataMathematicsRegression analysisRegressionAgricultureComputer scienceMachine learningGeographyAgronomyCartographyChemistryBiochemistryBiologyMetallurgyGeneArchaeologyMaterials scienceClimate change impacts on agricultureRemote Sensing in AgricultureSoybean genetics and cultivation