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 of seven morphological features extracted from the wing images and a support vector machine classifier to produce the final classification of Pusillus or Obsoletus class. The proposed app was validated on an experimental dataset with 87 samples, reaching an outstanding mean of AUC score of 0.98 in the classification stage. Besides, we assessed the app feasibility using the mean of time and battery consumption metrics on two different emulators. The obtained scores of 12 and 7 s and 0.11 and 0.03 mAh for the phone and tablet emulators are satisfactory when developing mobile applications. Finally, reducing the feature space using an external wrapper method provided us a considerable improvement in the classification performance, AUC scores from 0.95 to 0.98, and decreasing the volume of information in training stages. Thus, these results enable the proposed app as an excellent approximation to those specialists that need a practical tool to classify Pussillus or Obsoletus species in wildlife environments.