Combining Artificial Intelligence Services for the Recognition of Flora Photographs: Uses in Augmented Reality and Tourism

Abstract

Tourism information services are evolving rapidly. With Internet, tourists organize their trips by managing information before arriving at their destination. Nature is the main tourist attraction in Argentina. However, the information tools as field guides, have had few improvements in their digital version compared to printed ones. This work compares and combines machine learning services that includes deep learning, artificial intelligence and image recognition, to evaluate the app development for mobile phones that offer recognition of flora species in real time, in natural areas with low or no internet connectivity. Recognition of three Nothofagus tree species (with a dataset of 45 photos per species) were evaluated in the Tierra del Fuego National Park, using IBM Watson, Google Cloud and Microsoft Azure. Finally, we defined an algorithm combining those services to improve the results. Google Cloud was the service with the best performance recognizing all the tree species (83% effectiveness in average). The accuracy of Watson and Azure was lower than Google Cloud, and varied according to tree species. Combined algorithm improved the recognition with a 90% effectiveness in average. A next iteration of this work expects to increase the accuracy of recognition to get a total of 150 photos per specie into the dataset. We also expect to use assisted learning to improve the efficiency of the neural network obtained to know the adaptation capacities for each evaluated service.