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.