author = "Leonardo, Matheus Macedo and Faria, Fabio Augusto",
          affiliation = "{Federal University of Sao Paulo} and {Federal University of Sao 
                title = "Mid-level Image Representation for Fruit Crop Pest 
            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 = "fruit fly, local descriptor, insect recognition, image 
             abstract = "Fruit flies are of huge biological and economic importance for the 
                         farming of different countries in the World, especially for 
                         Brazil. Brazil is the third largest fruit producer in the world 
                         with 44 million tons in 2016. The direct and indirect losses 
                         caused by fruit flies can exceed USD 2 billion, putting these 
                         pests as one of the biggest problems of the world agriculture. In 
                         Brazil, it is estimated that the economic losses directly related 
                         to production, the cost of pest control and in the loss of export 
                         markets, are between USD 120 and 200 million/year. We propose to 
                         apply mid-level image representations based on local descriptors 
                         for fruit fly identification tasks of three species of the genus 
                         Anastrepha. In our experiments, several local image descriptors 
                         based on keypoints and machine learning techniques have been 
                         compared in the target task. Furthermore, the proposed approaches 
                         have achieved excellent effectiveness results when compared with a 
                         state-of-art technique.",
  conference-location = "Niter{\'o}i, RJ",
      conference-year = "Oct. 17-20, 2017",
             language = "en",
                  ibi = "8JMKD3MGPAW/3PJ4KFL",
                  url = "",
           targetfile = "wuw-moscas.pdf",
        urlaccessdate = "2021, Jan. 21"