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@InProceedings{LinsRied:2017:MeCoCl,
               author = "Lins, Elison Alfeu and Rieder, Rafael",
          affiliation = "{Universidade de Passo Fundo} and {Universidade de Passo Fundo}",
                title = "Uma metodologia de contagem e classifica{\c{c}}{\~a}o de 
                         af{\'{\i}}deos utilizando vis{\~a}o computacional",
            booktitle = "Proceedings...",
                 year = "2017",
               editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and 
                         Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and 
                         Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba, 
                         Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo 
                         and Vital, Creto and Pagot, Christian Azambuja and Petronetto, 
                         Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
         organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "OpenCv, tensorflow, aphids, counting, classification.",
             abstract = "AbstractAphids are insects that attack crops and cause damage 
                         directly, consuming the sap of plants, and indirectly, transmiting 
                         diseases. The counting and classification of these insects are 
                         fundamental for measuring and predicting crop hazards and serving 
                         as the basis for the application or not of chemicals. 
                         Traditionally, the counting process is manual, and depends of 
                         microscopes and good eyesight of the specialist, in a 
                         time-consuming task susceptible to errors. With this in mind, this 
                         paper presents a methodology and a software to automate the 
                         counting and classification of aphids using image processing, 
                         computer vision and deep learning methods. As preliminary results 
                         in a pilot study, we obtained 95.50 % correlation for the count of 
                         28 samples containing Rhopalosiphum padi, in shortest time 
                         compared the manual method.",
  conference-location = "Niter{\'o}i, RJ",
      conference-year = "Oct. 17-20, 2017",
             language = "pt",
                  ibi = "8JMKD3MGPAW/3PJ8FTL",
                  url = "http://urlib.net/rep/8JMKD3MGPAW/3PJ8FTL",
           targetfile = "2017_sibgrapi_WIP_Elison.pdf",
        urlaccessdate = "2021, Jan. 21"
}


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