Abstract
Culicoides biting midges are transmission vectors of various diseases affecting humans and animals around the world. An optimal and fast classification method for these and other species have been a challenge and a necessity, especially in areas with limited resources and public health problems. In this work, we developed a mobile application to classify two Culicoides species using the morphological pattern analysis of their wings. The app implemented an automatic classification method based on the calculation and reduction of seven morphological features extracted from the wing images, and a naive Bayes classifier to produce the final classification of C. pusillus or C. obsoletus class. The proposed app was validated on an experimental dataset with 87 samples, reaching an outstanding mean of the area under the curve of the receiver operating characteristic score of 0.973 in the classification stage. Besides, we assessed the app feasibility using the mean of execution time and battery consumption metrics on two different emulators. The obtained values of 5.54 and 4.35 s and 0.0.02 and 0.11 mAh for the tablet Pixel C and phone Pixel 2 emulators are satisfactory when developing mobile applications. The achieved results enable the proposed app as an excellent approximation of a practical tool for those specialists who need to classify C. pusillus or C. obsoletus species in wildlife settings.
Work funded by Universidad San Francisco de Quito (USFQ) through the Collaboration Grants (Grant no. 12476) and Chancellor Grants (Grant no. 1114) Programs.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Augot, D., et al.: Discrimination of Culicoides obsoletus and Culicoides scoticus, potential bluetongue vectors, by morphometrical and mitochondrial cytochrome oxidase subunit I analysis. Infect. Genet. Evol. 10(5), 629–637 (2010)
Banerjee, A.K., Kiran, K., Murty, U., Venkateswarlu, C.: Classification and identification of mosquito species using artificial neural networks. Comput. Biol. Chem. 32(6), 442–447 (2008)
Borkent, A.: The subgeneric classification of species of Culicoides (2014)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 1–27 (2011)
Developers Android: Android Debug Bridge (ADB). https://developer.android.com/studio/command-line/adb.html. Accessed 25 Mar 2020
Duda, R.O., Hart, P.E., et al.: Pattern Classification and Scene Analysis. Wiley, New York (2000)
Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing Using MATLAB. Pearson Education India (2004)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 10–18 (2009)
Henni, L.H., Sauvage, F., Ninio, C., Depaquit, J., Augot, D.: Wing geometry as a tool for discrimination of Obsoletus group (Diptera: Ceratopogonidae: Culicoides) in France. Infect. Genet. Evol. 21, 110–117 (2014)
Hollander, M., Wolfe, D.A., Chicken, E.: Nonparametric Statistical Methods. Wiley, Hoboken (1999)
Jain, Y.K., Bhandare, S.K.: Min max normalization based data perturbation method for privacy protection. Int. J. Comput. Commun. Technol. 2(8), 45–50 (2011)
Kaufmann, C., Schaffner, F., Ziegler, D., Pflueger, V., Mathis, A.: Identification of field-caught Culicoides biting midges using matrix-assisted laser desorption/ionization time of flight mass spectrometry. Parasitology 139(2), 248–258 (2012)
López, F.G., Torres, M.G., Batista, B.M., Pérez, J.A.M., Moreno-Vega, J.M.: Solving feature subset selection problem by a parallel scatter search. Eur. J. Oper. Res. 169(2), 477–489 (2006)
Meiswinkel, R., Baldet, T., De Deken, R., Takken, W., Delécolle, J.C., Mellor, P.S.: The 2006 outbreak of bluetongue in northern Europe–the entomological perspective. Prev. Vet. Med. 87(1–2), 55–63 (2008)
Meyer, F.: Topographic distance and watershed lines. Signal Process. 38(1), 113–125 (1994)
Motta, D., et al.: Application of convolutional neural networks for classification of adult mosquitoes in the field. PLoS ONE 14(1), (2019)
OpenCV Team: Open Source Computer Vision Documentation. https://docs.opencv.org/3.4.8/d1/dfb/intro.html. Accessed 08 Apr 2020
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)
Venegas, P., Pérez, N., Zapata, S., Mosquera, J.D., Augot, D., Rojo-Álvarez, J.L., Benítez, D.: An approach to automatic classification of Culicoides species by learning the wing morphology. PLoS ONE 15(11), e0241798 (2020)
Papadopoulos, A., Fotiadis, D.I., Likas, A.: Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines. Artif. Intell. Med. 34(2), 141–150 (2005)
Pérez, N.P., López, M.A.G., Silva, A., Ramos, I.: Improving the Mann-Whitney statistical test for feature selection: an approach in breast cancer diagnosis on mammography. Artif. Intell. Med. 63(1), 19–31 (2015)
Ramirez, P.G., Stein, M., Etchepare, E.G., Almiron, W.R.: Diversity of anopheline mosquitoes (Diptera: Culicidae) and classification based on the characteristics of the habitats where they were collected in Puerto Iguazú, Misiones, Argentina. J. Vector Ecol. 41(2), 215–223 (2016)
Rawlings, P.: A key, based on wing patterns of biting midges (genus Culicoides Latreille-Diptera: Ceratopogonidae) in the Iberian Peninsula, for use in epidemiological studies. Graellsia 52(11), 57–71 (1996)
Rosset, S.: Model selection via the AUC. In: Proceedings of the Twenty-First International Conference on Machine Learning, p. 89 (2004). https://doi.org/10.1145/1015330.1015400
Wang, G., et al.: Identifying the main mosquito species in China based on DNA barcoding. PLoS ONE 7(10) (2012)
Wang, S., Summers, R.M.: Machine learning and radiology. Med. Image Anal. 16(5), 933–951 (2012)
Acknowledgment
Authors thank the Applied Signal Processing and Machine Learning Research Group of USFQ for providing the computing infrastructure (NVidia DGX workstation) to implement and execute the developed source code.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Gutiérrez, S., Pérez, N., Benítez, D.S., Zapata, S., Augot, D. (2021). An Android App to Classify Culicoides Pusillus and Obsoletus Species. In: Orjuela-Cañón, A.D., Lopez, J., Arias-Londoño, J.D., Figueroa-García, J.C. (eds) Applications of Computational Intelligence. ColCACI 2020. Communications in Computer and Information Science, vol 1346. Springer, Cham. https://doi.org/10.1007/978-3-030-69774-7_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-69774-7_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-69773-0
Online ISBN: 978-3-030-69774-7
eBook Packages: Computer ScienceComputer Science (R0)