Close
Metadata

%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2017/09.04.15.12
%2 sid.inpe.br/sibgrapi/2017/09.04.15.12.42
%T Mid-level Image Representation for Fruit Crop Pest Identification
%D 2017
%8 Oct. 17-20, 2017
%A Leonardo, Matheus Macedo,
%A Faria, Fabio Augusto,
%@affiliation Federal University of Sao Paulo
%@affiliation Federal University of Sao Paulo
%E Torchelsen, Rafael Piccin,
%E Nascimento, Erickson Rangel do,
%E Panozzo, Daniele,
%E Liu, Zicheng,
%E Farias, Mylène,
%E Viera, Thales,
%E Sacht, Leonardo,
%E Ferreira, Nivan,
%E Comba, João Luiz Dihl,
%E Hirata, Nina,
%E Schiavon Porto, Marcelo,
%E Vital, Creto,
%E Pagot, Christian Azambuja,
%E Petronetto, Fabiano,
%E Clua, Esteban,
%E Cardeal, Flávio,
%B Conference on Graphics, Patterns and Images, 30 (SIBGRAPI)
%C Niterói, RJ
%S Proceedings
%I Sociedade Brasileira de Computação
%J Porto Alegre
%K fruit fly, local descriptor, insect recognition, image classification.
%X 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.
%@language en
%3 wuw-moscas.pdf


Close