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Reference TypeConference Proceedings
Last Update2018: administrator
Metadata Last Update2020: administrator
Citation KeyLeonardoCarRezZucFar:2018:DeFeCl
TitleDeep Feature-based Classifiers for Fruit Fly Identification (Diptera: Tephritidae)
DateOct. 29 - Nov. 1, 2018
Access Date2020, Dec. 04
Number of Files1
Size1375 KiB
Context area
Author1 Leonardo, Matheus M.
2 Carvalho, Tiago J.
3 Rezende, Edmar
4 Zucchi, Roberto
5 Faria, Fabio A.
Affiliation1 Universidade Federal de São Paulo
2 Federal Institute of São Paulo
3 University of Campinas
4 University of Sao Paulo
5 Universidade Federal de São Paulo
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
History2018-09-03 20:26:47 :: -> administrator ::
2020-02-19 03:10:44 :: administrator -> :: 2018
Content and structure area
Is the master or a copy?is the master
Document Stagecompleted
Document Stagenot transferred
Content TypeExternal Contribution
Tertiary TypeFull Paper
Keywordsdeep learning, classification, fruit fly, Anastrepha.
AbstractFruit 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.
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