期刊
JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
卷 25, 期 7, 页码 1516-1527出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jvcir.2014.06.014
关键词
Anastrepha; Multimodal fusion; Machine learning; Fraterculus group; Automatic identification; Classifier selection; Fruit flies; Wing and aculeus images
资金
- FAPESP [2010/14910-0, 2010/05647-4, 2009/54806-0, 98/05085-2]
- CNPq [303726/2009-1, 550890/2007-6, 309618/2010-0, 304352/2012-8]
- CAPES [1260-12-0]
- AMD
- Microsoft
- Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) [10/05647-4, 10/14910-0, 98/05085-2] Funding Source: FAPESP
Fruit flies are pests of major economic importance in agriculture. Among these pests it is possible to highlight some species of genus Anastrepha, which attack a wide range of fruits, and are widely distributed in the American tropics and subtropics. Researchers seek to identify fruit flies in order to implement management and control programs as well as quarantine restrictions. However, fruit fly identification is manually performed by scarce specialists through analysis of morphological features of the mesonotum, wing, and aculeus. Our objective is to find solid knowledge that can serve as a basis for the development of a sounding automatic identification system of the Anastrepha fraterculus group, which is of high economic importance in Brazil. Wing and aculeus images datasets from three specimens have been used in this work. The experiments using a classifier multimodal fusion approach shows promising effectiveness results for identification of these fruit flies, with more than 98% classification accuracy, a remarkable result for this difficult problem. (C) 2014 Elsevier Inc. All rights reserved.
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