Assisting Tourism in Underserved Areas with TensorFlow: A Proof-of-Concept Mobile App

摘要

Tourism contributes approximately 3% to the GDP in Africa. One of the big attraction to tourists visiting Africa is the wildlife - both the fauna and flora. The larger game reserves and national parks have a wide variety of supporting facililtes such as good access roads, accommodation, food services, and professional game rangers to assist tourists in identifying plants and animals. The smaller less known game reserves, however, often do not have the luxury of these supporting facilities and often lack professional game rangers. These reserves are often avoided by tourists. The surrounding communities, therefore, miss out on the additional economic advantage of having tourists in the area who also need to purchase petrol, buy food, and find accommodation. The paper investigates the use of Google TensorFlow in identifying African mammals. The actual TensorFlow model is trained using traditional desktop workstations and thousands of photographs. Once the model is created, it can be downloaded to an Android device and used in offline mode. This would allow tourists visiting smaller less known game reserves to identify animals and plants in areas where there is no Internet connectivity. The project was a proof-of-concept and the idea can be expanded to include bird watching clubs, fishing clubs, in addition to national parks. The results are very positive. The Android app developed for this research could reguarly identify 35 common African game mammals.