%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2017/09.05.12.21
%2 sid.inpe.br/sibgrapi/2017/09.05.12.21.35
%T Uma metodologia de contagem e classificação de afídeos utilizando visão computacional
%D 2017
%A Lins, Elison Alfeu,
%A Rieder, Rafael,
%@affiliation Universidade de Passo Fundo
%@affiliation Universidade de Passo Fundo
%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, Brazil
%8 17-20 Oct. 2017
%I Sociedade Brasileira de Computação
%J Porto Alegre
%S Proceedings
%K OpenCv, tensorflow, aphids, counting, classification.
%X AbstractAphids are insects that attack crops and cause damage directly, consuming the sap of plants, and indirectly, transmiting diseases. The counting and classification of these insects are fundamental for measuring and predicting crop hazards and serving as the basis for the application or not of chemicals. Traditionally, the counting process is manual, and depends of microscopes and good eyesight of the specialist, in a time-consuming task susceptible to errors. With this in mind, this paper presents a methodology and a software to automate the counting and classification of aphids using image processing, computer vision and deep learning methods. As preliminary results in a pilot study, we obtained 95.50 % correlation for the count of 28 samples containing Rhopalosiphum padi, in shortest time compared the manual method.
%@language pt
%3 2017_sibgrapi_WIP_Elison.pdf