%0 Conference Proceedings
%A Leonardo, Matheus M.,
%A Carvalho, Tiago J.,
%A Rezende, Edmar,
%A Zucchi, Roberto,
%A Faria, Fabio A.,
%@affiliation Universidade Federal de São Paulo
%@affiliation Federal Institute of São Paulo
%@affiliation University of Campinas
%@affiliation University of Sao Paulo
%@affiliation Universidade Federal de São Paulo
%T Deep Feature-based Classifiers for Fruit Fly Identification (Diptera: Tephritidae)
%B Conference on Graphics, Patterns and Images, 31 (SIBGRAPI)
%D 2018
%E Ross, Arun,
%E Gastal, Eduardo S. L.,
%E Jorge, Joaquim A.,
%E Queiroz, Ricardo L. de,
%E Minetto, Rodrigo,
%E Sarkar, Sudeep,
%E Papa, João Paulo,
%E Oliveira, Manuel M.,
%E Arbeláez, Pablo,
%E Mery, Domingo,
%E Oliveira, Maria Cristina Ferreira de,
%E Spina, Thiago Vallin,
%E Mendes, Caroline Mazetto,
%E Costa, Henrique Sérgio Gutierrez,
%E Mejail, Marta Estela,
%E Geus, Klaus de,
%E Scheer, Sergio,
%S Proceedings
%8 Oct. 29 - Nov. 1, 2018
%J Los Alamitos
%I IEEE Computer Society
%C Foz do Iguaçu, PR, Brazil
%K deep learning, classification, fruit fly, Anastrepha.
%X 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.
%@language en
%3 deep-feature-based.pdf