Comparison of services for the recognition of flora images : uses in augmented reality and tourism
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ArticleDescription: 1 archivo (264,3 kB)Subject(s): Summary: 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 machine learning, deep learning, artificial intelligence and image recognition services, to evaluate the app development for mobile phones that offers the recognition in real time of flora species in natural areas with low or no internet connectivity. Recognition of three Nothofagus tree species were evaluated in the Tierra del Fuego National Park, using IBM Watson and Microsoft Azure, with good results in general. A next iteration of this work expects to use assisted learning to improve the efficiency of the neural network obtained to know the adaptation capacities for each evaluated service.
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Capítulo de libro
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Biblioteca de la Facultad de Informática | Biblioteca digital | A1087 (Browse shelf(Opens below)) | Link to resource | No corresponde |
Formato de archivo PDF. -- Este documento es producción intelectual de la Facultad de Informática - UNLP (Colección BIPA/Biblioteca)
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 machine learning, deep learning, artificial intelligence and image recognition services, to evaluate the app development for mobile phones that offers the recognition in real time of flora species in natural areas with low or no internet connectivity. Recognition of three Nothofagus tree species were evaluated in the Tierra del Fuego National Park, using IBM Watson and Microsoft Azure, with good results in general. A next iteration of this work expects to use assisted learning to improve the efficiency of the neural network obtained to know the adaptation capacities for each evaluated service.
Congreso Argentino de Ciencias de la Computación (24to : 2018 : Tandil, Argentina)