Smart surveillance with conversational alerts for wild elephant early warning
| dc.contributor.author | Kongyim, Nattaya | |
| dc.contributor.author | Kaitwanidvilai, Somyot | |
| dc.date.accessioned | 2026-08-06T10:54:01Z | |
| dc.date.available | 2026-08-06T10:54:01Z | |
| dc.date.issued | 2026-01-01 | |
| dc.description.abstract | Human-elephant conflict continues to threaten communities near forested areas, particularly in rural regions where wild elephant intrusions endanger lives and agricultural livelihoods. This study presents a real-time AI-powered surveillance and alert system integrating YOLOv8-based object detection, a localized CCTV network, and a human-like conversational agent on the LINE platform. Unlike traditional systems relying on expensive cloud computing and commercial messaging services, this solution employs a free communication channel and a custom-built bot that delivers timely alerts—complete with images, GPS location, and timestamp—directly to local residents and authorities. The object detection model, trained on localized datasets, achieved a precision of 98.9%, recall of 97.3%, F1-score of 98.1%, and mAP@0.5 of 98.6%. The end-to-end response time—from detection to alert delivery—averaged just 3.8 s. Enhanced by strong Wi-Fi antenna deployment, the system enables rapid, wide-area data dissemination without relying on stable internet or costly mobile networks. This significantly reduces data charges, a critical barrier in the deployment of smart city technologies in rural areas. Comparative analysis with conventional manual patrols and cloud-based AI systems confirms that the proposed approach is not only faster and more accurate but also far more cost-effective. Field tests validate its robustness in diverse lighting conditions, with rare false positives primarily under visually ambiguous scenarios. Feedback from local stakeholders further highlights its practical utility in improving situational awareness, response time, and long-term conflict mitigation. This system demonstrates a scalable and sustainable model for smart, AI-driven wildlife monitoring in developing regions. | |
| dc.identifier.citation | Remote Sensing Applications Society and Environment, 41, 2026 | |
| dc.identifier.doi | 10.1016/j.rsase.2026.101924 | |
| dc.identifier.issn | 23529385 | |
| dc.identifier.other | 2-s2.0-105030864216 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/17717 | |
| dc.source | Remote Sensing Applications Society and Environment | |
| dc.subject | CCTV surveillance | |
| dc.subject | Community alert system | |
| dc.subject | Human-wildlife conflict | |
| dc.subject | Machine learning | |
| dc.subject | Real-time detection | |
| dc.subject | Wild elephant intrusion | |
| dc.title | Smart surveillance with conversational alerts for wild elephant early warning | |
| dc.type | Article |