@InProceedings{LeonardoCarRezZucFar:2018:DeFeCl,
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 = "29 Oct.-1 Nov. 2018",
doi = "10.1109/SIBGRAPI.2018.00012",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2018.00012",
language = "en",
ibi = "8JMKD3MGPAW/3RPAD4E",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3RPAD4E",
targetfile = "deep-feature-based.pdf",
urlaccessdate = "2024, May 05"
}