02265naa a2200385 a 450000100080000000500110000800800410001902000220006002400510008210000200013324500870015326000090024030000140024952010430026365000160130665000190132265000280134165000330136965000180140265000100142065000250143065300180145565300320147365300260150565300210153165300100155265300180156265300170158065300190159770000260161670000220164270000200166470000230168477301720170721560562023-08-23       2023    bl uuuu     u00u1 u    #d  a978-65-258-1530-57 ahttps://doi.org/10.22533/at.ed.30523020832DOI1 aCARNEIRO, F. M.  aMachine learning approach for corn nitrogen recommendation.h[electronic resource]  c2023  ap. 21-28.  aNitrogen (N) fertilizer recommendation tools are vital to precise agricultural management. The objectives of this research were to determine how many variables and remote sensor dataare needed to prescribe N fertilizer in corn (Zea mays L.), PFP (partial factor productivity), and yield integrating remote sensing and soil sensor technologies. The variables of this work were NIR, Red, Red-Edge wavelengths, plant height, canopy temperature, LAI (leaf area index), and apparent soil electrical conductivity (ECa). Random Forest Classifier was used to select the best input to estimate N rates, PFP, and corn yield. A confusion matrix was used to identify the accuracy of the Random Forest Classifier to detect the best inputs to estimate for which input we evaluated in this work. According to Random Forest, the best inputs to estimate the N rate and PFP were Red-Edge, Red, and NIR wavelengths, plant height, and canopy temperature. For estimate corn yield were: NIR wavelengths, N rates, plant height, Red-Edge, and canopy temperature.  aEnvironment  aRemote sensing  aSustainable development  aDesenvolvimento Sustentável  aMeio Ambiente  aMilho  aSensoriamento Remoto  aActive sensor  aEstimativa de produtividade  aLinguagem de máquina  aMachine learning  aMaize  aRandom Forest  aSensor ativo  aYield estimate1 aBRITO FILHO, A. L. de1 aMARTINS, M. de S.1 aBRANDÃO, Z. N.1 aSHIRATSUCHI, L. S.  tIn: SILVA-MATOS, R. R. S. da; DOIHARA, I. P.; LINHARES, S. C. (org.). Medio ambiente: Agricultura, desarrollo y sustentabilidad 2. Ponta Grossa, PR: Atena, 2023. c. 3.