@InProceedings{CarneiroBriMarBraShi:2023:NiReBa,
               author = "Carneiro, Franciele Morlin and Brito Filho, Armando Lopes de and 
                         Martins, Murilo de Santana and Brand{\~a}o, Ziany Neiva and 
                         Shiratsuchi, Luciano Shozo",
          affiliation = "{Universidade Tecnol{\'o}gica Federal do Paran{\'a} (UTFPR)} and 
                         {Universidade Estadual Paulista (UNESP)} and {Louisiana State 
                         University (LSU)} and {Empresa Brasileira de Pesquisa 
                         Agropecu{\'a}ria (EMBRAPA)} and {Louisiana State University 
                         (LSU)}",
                title = "Nitrogen recommendation based on machine learning approach and 
                         active remote sensing",
            booktitle = "Anais...",
                 year = "2023",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de and Sanches, Ieda DelArco",
                pages = "e156257",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 20. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "active sensor, Random Forest, remote sensing, corn, yield 
                         estimate.",
             abstract = "Nitrogen (N) fertilizer recommendation tools are vital to precise 
                         agricultural management. The objectives of this research were to 
                         determine how many variables and remote sensor data are needed to 
                         prescribe N fertilizer in corn, 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, and apparent soil 
                         electrical. 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.",
  conference-location = "Florian{\'o}polis",
      conference-year = "02-05 abril 2023",
                 isbn = "978-65-89159-04-9",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/48TQS38",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/48TQS38",
           targetfile = "156257.pdf",
                 type = "Sistemas sensores: projeto, calibra{\c{c}}{\~a}o e 
                         avalia{\c{c}}{\~a}o",
        urlaccessdate = "27 abr. 2024"
}