The use of drones and Artificial Intelligence for dugong sighting detection in a limited resource scenario
The use of commercially available drones and artificial intelligence (AI) has grown in popularity in the last decade. Nonetheless, its usage to detect cryptic and high-mobility marine mammals remains constrained by resource-intensive nature, vast coverage areas, hardware limitations, and environment...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | BIO Web of Conferences |
| Online Access: | https://www.bio-conferences.org/articles/bioconf/pdf/2025/07/bioconf_icfaes24_01004.pdf |
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| author | Ario Digdo Akbar Astari Elisabeth Riskyta Arinda Bella Cahyono Topan |
| author_facet | Ario Digdo Akbar Astari Elisabeth Riskyta Arinda Bella Cahyono Topan |
| author_sort | Ario Digdo Akbar |
| collection | DOAJ |
| description | The use of commercially available drones and artificial intelligence (AI) has grown in popularity in the last decade. Nonetheless, its usage to detect cryptic and high-mobility marine mammals remains constrained by resource-intensive nature, vast coverage areas, hardware limitations, and environmental variables. This study aims to recount our experience conducting a combination of drone and AI-assisted detection (WISDAM) in a scenario with limited resources to detect dugongs. The operation was conducted in September 2023, April 2024, and May 2024 in North Sulawesi, Indonesia. Prior to flight path design, CMS questionnaires and satellite data were utilized in order to comprehend the spatial and temporal context of the dugongs and their preferred habitat. A DJI Air 2s drone was used in 28 flights, covering 12.09 km2, yielding 8,509 photos. In total, 47 photos comprise dugongs, including seven with multiple individuals. 56 sightings were successfully identified manually by multiple analysts to minimize bias, and seven photos (12.5%) were considered dubious. AI detection is rather limited compared with manual detection’s numbers of positively identified dugong photos. Out of 153 AI detections, only 27 (17.6%) were True Positives. Therefore, more flights are needed to enhance the sample size for machine learning. |
| format | Article |
| id | doaj-art-d33f239ff4dc4bc482ec84e870797df7 |
| institution | Kabale University |
| issn | 2117-4458 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | BIO Web of Conferences |
| spelling | doaj-art-d33f239ff4dc4bc482ec84e870797df72025-02-05T10:43:32ZengEDP SciencesBIO Web of Conferences2117-44582025-01-011560100410.1051/bioconf/202515601004bioconf_icfaes24_01004The use of drones and Artificial Intelligence for dugong sighting detection in a limited resource scenarioArio Digdo Akbar0Astari Elisabeth1Riskyta Arinda Bella2Cahyono Topan3YAPEKA, Kota BogorYAPEKA, Kota BogorYAPEKA, Kota BogorYAPEKA, Kota BogorThe use of commercially available drones and artificial intelligence (AI) has grown in popularity in the last decade. Nonetheless, its usage to detect cryptic and high-mobility marine mammals remains constrained by resource-intensive nature, vast coverage areas, hardware limitations, and environmental variables. This study aims to recount our experience conducting a combination of drone and AI-assisted detection (WISDAM) in a scenario with limited resources to detect dugongs. The operation was conducted in September 2023, April 2024, and May 2024 in North Sulawesi, Indonesia. Prior to flight path design, CMS questionnaires and satellite data were utilized in order to comprehend the spatial and temporal context of the dugongs and their preferred habitat. A DJI Air 2s drone was used in 28 flights, covering 12.09 km2, yielding 8,509 photos. In total, 47 photos comprise dugongs, including seven with multiple individuals. 56 sightings were successfully identified manually by multiple analysts to minimize bias, and seven photos (12.5%) were considered dubious. AI detection is rather limited compared with manual detection’s numbers of positively identified dugong photos. Out of 153 AI detections, only 27 (17.6%) were True Positives. Therefore, more flights are needed to enhance the sample size for machine learning.https://www.bio-conferences.org/articles/bioconf/pdf/2025/07/bioconf_icfaes24_01004.pdf |
| spellingShingle | Ario Digdo Akbar Astari Elisabeth Riskyta Arinda Bella Cahyono Topan The use of drones and Artificial Intelligence for dugong sighting detection in a limited resource scenario BIO Web of Conferences |
| title | The use of drones and Artificial Intelligence for dugong sighting detection in a limited resource scenario |
| title_full | The use of drones and Artificial Intelligence for dugong sighting detection in a limited resource scenario |
| title_fullStr | The use of drones and Artificial Intelligence for dugong sighting detection in a limited resource scenario |
| title_full_unstemmed | The use of drones and Artificial Intelligence for dugong sighting detection in a limited resource scenario |
| title_short | The use of drones and Artificial Intelligence for dugong sighting detection in a limited resource scenario |
| title_sort | use of drones and artificial intelligence for dugong sighting detection in a limited resource scenario |
| url | https://www.bio-conferences.org/articles/bioconf/pdf/2025/07/bioconf_icfaes24_01004.pdf |
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