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Reference TypeConference Paper (Conference Proceedings)
Last Update2017:
Metadata Last Update2020: administrator
Citation KeyLeonardoFari:2017:MiImRe
TitleMid-level Image Representation for Fruit Crop Pest Identification
Access Date2021, Jan. 26
Number of Files1
Size460 KiB
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Author1 Leonardo, Matheus Macedo
2 Faria, Fabio Augusto
Affiliation1 Federal University of Sao Paulo
2 Federal University of Sao Paulo
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ
DateOct. 17-20, 2017
Book TitleProceedings
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Tertiary TypeUndergraduate Work
History2017-09-04 15:12:42 :: -> administrator ::
2020-02-20 22:06:46 :: administrator -> :: 2017
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Is the master or a copy?is the master
Content Stagecompleted
Keywordsfruit fly, local descriptor, insect recognition, image classification.
AbstractFruit 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.
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