01324naa a2200349 a 450000100080000000500110000800800410001902400570006010000260011724501320014326000090027552002210028465000110050565000140051665000210053065000210055165000260057265300240059865300240062265300370064665300340068365300210071765300380073865300090077670000200078570000220080570000220082770000230084970000200087270000260089277300560091821530062023-04-05       2023    bl uuuu     u00u1 u    #d7 ahttps://doi.org/10.3390/ agriengineering50200442DOI1 aVASCONCELOS, J. C. S.  aDevelopment and validation of a model based on vegetation indices for the prediction of sugarcane yield.h[electronic resource]  c2023  aThis study aimed to develop a predictive model for sugarcane production based on data extracted from aerial imagery obtained from drones or satellites, allowing the precise tracking of plant development in the field.  aModels  aSugarcane  aVegetation index  aCana de Açúcar  aSaccharum Officinarum  aAgricultura digital  aDigital agriculture  aDistribuição gaussiana inversa  aInverse Gaussian distribution  aModelo preditivo  aRemotely piloted aircraft systems  aRPAS1 aSPERANZA, E. A.1 aANTUNES, J. F. G.1 aBARBOSA, L. A. F.1 aCHRISTOFOLETTI, D.1 aSEVERINO, F. J.1 aCANÇADO, G. M. de A.  tAgriEngineeringgv. 5, n. 2, p. 698-719, June 2023.