International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12
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 Volume 11, Issue 3 (March 2024), Pages: 36-45

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 Original Research Paper

Developing a plogging activity tracking app using deep learning for image recognition

 Author(s): 

 Jung-Been Lee *, Taek Lee, Jeong-Dong Kim, In-Hye Yoo, Da-Bin Kim, Jung-Yeon Park

 Affiliation(s):

 Division of Computer Science and Engineering, Sun Moon University, Asan, South Korea

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-8208-0387

 Digital Object Identifier (DOI)

 https://doi.org/10.21833/ijaas.2024.03.005

 Abstract

Plogging is an activity that combines jogging with picking up litter, and participants often share their efforts on social media. However, the repetitive bending involved in plogging may cause back strain, and manually entering details such as the location and quantity of litter could slow the spread of this activity. This study sought to create and test a deep learning application to automatically monitor and record plogging by identifying the type and quantity of litter. We employed Convolutional Neural Networks (CNN) and YOLOv5 to develop an image recognition model. This model allowed users to easily log their plogging efforts by simply taking a photograph, removing the need to manually input the litter details. Moreover, we proposed a reward system that uses the collected trash amount and the distance covered to promote competition among users. We developed the first application that uses deep learning to automatically identify litter for tracking plogging activities. However, as this application was only a prototype, no comparative studies or usability tests were done. In future research, we plan to assess the application's usability and compare it with other similar applications.

 © 2024 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Deep learning, Object classification, Mobile application, Trash management, Plogging

 Article history

 Received 1 September 2023, Received in revised form 3 January 2024, Accepted 6 February 2024

 Acknowledgment 

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (RS-2023-00243114), the MISP (Ministry of Science, ICT Future Planning), Korea, under the National Program for Excellence in SW) supervised by the IITP (Institute for Information and communications Technology Promotion), and the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (HI22C1472).

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Lee JB, Lee T, Kim JD, Yoo IH, Kim DB, and Park JY (2024). Developing a plogging activity tracking app using deep learning for image recognition. International Journal of Advanced and Applied Sciences, 11(3): 36-45

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 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7 Fig. 8 Fig. 9 Fig. 10 Fig. 11 Fig. 12 

 Tables

 Table 1 

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