author = "Leonardo, Matheus M. and Carvalho, Tiago J. and Rezende, Edmar and 
                         Zucchi, Roberto and Faria, Fabio A.",
          affiliation = "{Universidade Federal de S{\~a}o Paulo} and {Federal Institute of 
                         S{\~a}o Paulo} and {University of Campinas} and {University of 
                         Sao Paulo} and {Universidade Federal de S{\~a}o Paulo}",
                title = "Deep Feature-based Classifiers for Fruit Fly Identification 
                         (Diptera: Tephritidae)",
            booktitle = "Proceedings...",
                 year = "2018",
               editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and 
                         Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and 
                         Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez, 
                         Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de 
                         and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa, 
                         Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus, 
                         Klaus de and Scheer, Sergio",
         organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "deep learning, classification, fruit fly, Anastrepha.",
             abstract = "Fruit flies has a big biological and economic im- portance for the 
                         farming of different tropical and subtropical countries in the 
                         World. Specifically in Brazil, third largest fruit producer in the 
                         world, the direct and indirect losses caused by fruit flies can 
                         exceed USD 120 million/year. These losses are related to 
                         production, the cost of pest control and export markets. One of 
                         the most economically important fruit flies in the America belong 
                         to the genus Anastrepha, which has approximately 300 known 
                         species, of which 120 are recorded in Brazil. However, less than 
                         10 species are economically important and are considered pests of 
                         quarantine significance by regulatory agencies. The extreme 
                         similarity among the species of the genus Anastrepha makes its 
                         manual taxonomic classification a nontrivial task, causing onerous 
                         and very subjective results. In this work, we propose an approach 
                         based on deep learning to assist the scarce specialists, reducing 
                         the time of analysis, subjectivity of the classifications and 
                         consequently, the economic losses related to these agricultural 
                         pests. In our experiments, five deep features and nine machine 
                         learning techniques have been studied for the target task. 
                         Furthermore, the proposed approach have achieved similar 
                         effectiveness results to state-of-art approaches.",
  conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
      conference-year = "Oct. 29 - Nov. 1, 2018",
             language = "en",
           targetfile = "deep-feature-based.pdf",
        urlaccessdate = "2020, Dec. 04"