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A deep learning plants identification model for augmented reality touring in urban parks
Authors: Konstantinos Ferentinos, Myrto Barda
Identifier: CSBE21477
Download file: https://library.csbe-scgab.ca/docs/meetings/2021/CSBE21477.pdf
Published in: CSBE-SCGAB Technical Conferences » 5th CIGR and AGM Quebec City 2021 » Regular Sessions
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Description: Urban parks usually contain a wide variety of plant and tree species, which form suitable conditions for the attraction of city residents and visitors. Enhancing the touring experience of these areas, can increase the environmental awareness among citizens and assist in the development of alternative forms of ?green tourism?. New technologies are a key tool in enhancing the experience of touring urban parks, as they can make park visits much more entertaining and at the same time educative, highlighting interesting information about the flora and fauna of the park, as well as various other points of interest. In the context of an ongoing research project called ?VR-Park?, an augmented reality system is developed for the enhancement of the urban park touring experience. In this work, the development of a computational model for the automatic identification of specific plant and tree species through real-time video of a smartphone or tablet device is presented. The model is based on deep learning methodologies (convolutional neural networks) for fast object identification in video stream, and 24 different plants and trees are identified. The model is tested and assessed in real conditions in an urban park. It will be eventually incorporated into the augmented reality application developed in the VR-Park project, to enhance the user experience by presenting information about the identified plants and trees, and thus transforming the system into an agriculture-specific educational tool which would further improve the park visitor?s experience.
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Date: 2021-06-11
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Conference name: 5th CIGR International Conference and CSBE-SCGAB AGM 2021, Quebec City,QC, 11-14 May 2021.
Session name: Monitoring, Control and Data Analysis 2
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Type: Text.Abstract
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Publication type: Presentation
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Coverage: Greece
Language 1: en
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Rights: Canadian Society for Bioengineering
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DOI:
Description: Urban parks usually contain a wide variety of plant and tree species, which form suitable conditions for the attraction of city residents and visitors. Enhancing the touring experience of these areas, can increase the environmental awareness among citizens and assist in the development of alternative forms of ?green tourism?. New technologies are a key tool in enhancing the experience of touring urban parks, as they can make park visits much more entertaining and at the same time educative, highlighting interesting information about the flora and fauna of the park, as well as various other points of interest. In the context of an ongoing research project called ?VR-Park?, an augmented reality system is developed for the enhancement of the urban park touring experience. In this work, the development of a computational model for the automatic identification of specific plant and tree species through real-time video of a smartphone or tablet device is presented. The model is based on deep learning methodologies (convolutional neural networks) for fast object identification in video stream, and 24 different plants and trees are identified. The model is tested and assessed in real conditions in an urban park. It will be eventually incorporated into the augmented reality application developed in the VR-Park project, to enhance the user experience by presenting information about the identified plants and trees, and thus transforming the system into an agriculture-specific educational tool which would further improve the park visitor?s experience.
Keywords:
Résumé:
Mots-clés:
Citation:
Volume:
Issue:
Pages -
Contributor:
Date: 2021-06-11
Technical field:
Conference name: 5th CIGR International Conference and CSBE-SCGAB AGM 2021, Quebec City,QC, 11-14 May 2021.
Session name: Monitoring, Control and Data Analysis 2
Other information:
Type: Text.Abstract
Format:
Publication type: Presentation
Source:
Relation:
Coverage: Greece
Language 1: en
Language 2:
Rights: Canadian Society for Bioengineering
Notes:
Other files:
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