In this study, we assess image analysis techniques as automatic identifiers of three Anastrepha species of quarantine importance, Anastrepha fraterculus (Wiedemann), Anastrepha obliqua (Macquart), and Anastrepha sororcula Zucchi, based on wing and aculeus images. The right wing and aculeus of 100 individuals of each species were mounted on microscope slides, and images were captured with a stereomicroscope and light microscope. For wing image analysis, we used the color descriptor Local Color Histogram; for aculei, we used the contour descriptor Edge Orientation Autocorrelogram. A Support Vector Machine classifier was used in the final stage of wing and aculeus classification. Very accurate species identifications were obtained based on wing and aculeus images, with average accuracies of 94 and 95%, respectively. These results are comparable to previous identification results based on morphometric techniques and to the results achieved by experienced entomologists. Wing and aculeus images produced equally accurate classifications, greatly facilitating the identification of these species. The proposed technique is therefore a promising option for separating these three closely related species in the fraterculus group.
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PP was supported by a graduate scholarship from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES). LRJ was supported by graduate and post-doctoral grants by Fapesp (grants 09/54806-0 and 14/16082-9). RAZ, RST, and AR are research fellows of the National Council for Scientific Research (CNPq). This study is part of the dissertation presented by PP to obtain a M.Sc. in Entomology at ESALQ/University of São Paulo.
Edited by Ranyse B Querino – Embrapa
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Perre, P., Faria, F.A., Jorge, L.R. et al. Toward an Automated Identification of Anastrepha Fruit Flies in the fraterculus group (Diptera, Tephritidae). Neotrop Entomol 45, 554–558 (2016). https://doi.org/10.1007/s13744-016-0403-0
- image analysis
- machine learning