@InProceedings{PereiraBugaSait:2016:ApAtCl,
               author = "Pereira, Douglas Felipe and Bugatti, Pedro Henrique and Saito, 
                         Priscila Tiemi Maeda",
          affiliation = "{Federal University of Technology (UTFPR)} and {Federal University 
                         of Technology (UTFPR)} and Federal University of Technology 
                         (UTFPR), University of Campinas (UNICAMP)",
                title = "Aprendizado ativo para classifica{\c{c}}{\~a}o do vigor de 
                         sementes de soja",
            booktitle = "Proceedings...",
                 year = "2016",
               editor = "Aliaga, Daniel G. and Davis, Larry S. and Farias, Ricardo C. and 
                         Fernandes, Leandro A. F. and Gibson, Stuart J. and Giraldi, Gilson 
                         A. and Gois, Jo{\~a}o Paulo and Maciel, Anderson and Menotti, 
                         David and Miranda, Paulo A. V. and Musse, Soraia and Namikawa, 
                         Laercio and Pamplona, Mauricio and Papa, Jo{\~a}o Paulo and 
                         Santos, Jefersson dos and Schwartz, William Robson and Thomaz, 
                         Carlos E.",
         organization = "Conference on Graphics, Patterns and Images, 29. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "aprendizado ativo, an{\'a}lise de imagens, processamento de 
                         imagens, classifica{\c{c}}{\~a}o, sementes de soja.",
             abstract = "The task of providing a high quality grain (e.g. soybean) to the 
                         farmer is a key challenge of the agrobusiness field. To achieve 
                         such quality considering soybean seeds it is applied the so-called 
                         tetrazolium test. This test provides an acurate diagnosis of the 
                         damages found in the seed, such as lacerations caused by insects, 
                         mechanical damages or high rates of humidity. These damages cause 
                         a considerable quality reduction and directly impact in the seed 
                         vigor. Some traditional machine learning methods were applied to 
                         the context of seed crops, in order to automatic classify the seed 
                         vigor. However, the great majority of the researches use the 
                         traditional supervised learning paradigm. Thus, in this paper we 
                         proposed to exploit the active learning paradigm to perform the 
                         classification of the seed vigor, derived from the tetrazolium 
                         test.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
             language = "pt",
                  ibi = "8JMKD3MGPAW/3M8SCP5",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3M8SCP5",
           targetfile = "2016-sibgrapi-wip.pdf",
        urlaccessdate = "2024, May 02"
}