A Framework for Detecting and Managing Non-Point-Source Pollution in Agricultural Areas Using GeoAI and UAVs
This study proposes a novel framework for detecting and managing non-point-source (NPS) pollution in agricultural areas using unmanned aerial vehicles (UAVs) and geospatial artificial intelligence (GeoAI). High-resolution UAV imagery, combined with the YOLOv8 instance segmentation model, was employe...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/8/12/786 |
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| author | Miso Park Heung-Min Kim Youngmin Kim Suho Bak Tak-Young Kim Seon Woong Jang |
| author_facet | Miso Park Heung-Min Kim Youngmin Kim Suho Bak Tak-Young Kim Seon Woong Jang |
| author_sort | Miso Park |
| collection | DOAJ |
| description | This study proposes a novel framework for detecting and managing non-point-source (NPS) pollution in agricultural areas using unmanned aerial vehicles (UAVs) and geospatial artificial intelligence (GeoAI). High-resolution UAV imagery, combined with the YOLOv8 instance segmentation model, was employed to accurately detect and classify various NPS sources, such as livestock barns, compost heaps, greenhouses, and mulching films. The spatial information, including the area and volume of detected objects, was analyzed to track temporal changes and evaluate management strategies. The framework integrates remote sensing, deep learning, and geographic information system (GIS) analysis to enhance decision-making processes, providing detailed insight into NPS pollution dynamics over time. This approach not only improves the efficiency of NPS monitoring but also facilitates proactive management by offering precise location and environmental impact data. The results indicate that this framework can significantly improve resource allocation and environmental management practices, particularly in agriculture-dominated regions susceptible to NPS pollution, thereby contributing to the sustainable development of these areas. |
| format | Article |
| id | doaj-art-c5d5efa3b4544585873d77fb8a8628c6 |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-c5d5efa3b4544585873d77fb8a8628c62024-12-27T14:21:57ZengMDPI AGDrones2504-446X2024-12-0181278610.3390/drones8120786A Framework for Detecting and Managing Non-Point-Source Pollution in Agricultural Areas Using GeoAI and UAVsMiso Park0Heung-Min Kim1Youngmin Kim2Suho Bak3Tak-Young Kim4Seon Woong Jang5Research Institute, IREMTECH Co., Ltd., Busan 46027, Republic of KoreaResearch Institute, IREMTECH Co., Ltd., Busan 46027, Republic of KoreaResearch Institute, IREMTECH Co., Ltd., Busan 46027, Republic of KoreaResearch Institute, IREMTECH Co., Ltd., Busan 46027, Republic of KoreaRemote Sensing Department, IREMTECH Co., Ltd., Busan 46027, Republic of KoreaResearch Institute, IREMTECH Co., Ltd., Busan 46027, Republic of KoreaThis study proposes a novel framework for detecting and managing non-point-source (NPS) pollution in agricultural areas using unmanned aerial vehicles (UAVs) and geospatial artificial intelligence (GeoAI). High-resolution UAV imagery, combined with the YOLOv8 instance segmentation model, was employed to accurately detect and classify various NPS sources, such as livestock barns, compost heaps, greenhouses, and mulching films. The spatial information, including the area and volume of detected objects, was analyzed to track temporal changes and evaluate management strategies. The framework integrates remote sensing, deep learning, and geographic information system (GIS) analysis to enhance decision-making processes, providing detailed insight into NPS pollution dynamics over time. This approach not only improves the efficiency of NPS monitoring but also facilitates proactive management by offering precise location and environmental impact data. The results indicate that this framework can significantly improve resource allocation and environmental management practices, particularly in agriculture-dominated regions susceptible to NPS pollution, thereby contributing to the sustainable development of these areas.https://www.mdpi.com/2504-446X/8/12/786unmanned aerial vehiclegeospatial artificial intelligencenon-point-source pollutiondeep learningenvironmental monitoring |
| spellingShingle | Miso Park Heung-Min Kim Youngmin Kim Suho Bak Tak-Young Kim Seon Woong Jang A Framework for Detecting and Managing Non-Point-Source Pollution in Agricultural Areas Using GeoAI and UAVs Drones unmanned aerial vehicle geospatial artificial intelligence non-point-source pollution deep learning environmental monitoring |
| title | A Framework for Detecting and Managing Non-Point-Source Pollution in Agricultural Areas Using GeoAI and UAVs |
| title_full | A Framework for Detecting and Managing Non-Point-Source Pollution in Agricultural Areas Using GeoAI and UAVs |
| title_fullStr | A Framework for Detecting and Managing Non-Point-Source Pollution in Agricultural Areas Using GeoAI and UAVs |
| title_full_unstemmed | A Framework for Detecting and Managing Non-Point-Source Pollution in Agricultural Areas Using GeoAI and UAVs |
| title_short | A Framework for Detecting and Managing Non-Point-Source Pollution in Agricultural Areas Using GeoAI and UAVs |
| title_sort | framework for detecting and managing non point source pollution in agricultural areas using geoai and uavs |
| topic | unmanned aerial vehicle geospatial artificial intelligence non-point-source pollution deep learning environmental monitoring |
| url | https://www.mdpi.com/2504-446X/8/12/786 |
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