Bovitrack:Animal behavior monitoring using Machine learning and IoT
| dc.contributor.author | Viraj, H | |
| dc.contributor.author | Wijesekara, S | |
| dc.contributor.author | Tharuka, K | |
| dc.contributor.author | Fernando, S | |
| dc.contributor.author | Jayakody, A | |
| dc.contributor.author | Wijesiri, P | |
| dc.date.accessioned | 2026-03-22T09:00:16Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | Analyzing dairy cattle behavior and anomalies is a critical component of precision livestock farming, allowing farmers to remotely monitor animals for health and behavior. In order to accomplish this task better, the use of IoT technology and machine learning algorithms is more appropriate as per the time. The YOLO (you only look once) object recognition algorithm is more suitable for that, and the use of this algorithm allows these processes to be performed automatically and in real time with high accuracy. YOLO's ability to recognize multiple objects in images or videos makes Yolo ideal for cattle detection and tracking. | |
| dc.identifier.doi | DOI: 10.1109/ICARC64760.2025.10963308 | |
| dc.identifier.isbn | 979-833153098-3 | |
| dc.identifier.uri | https://rda.sliit.lk/handle/123456789/4906 | |
| dc.language.iso | en | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.relation.ispartofseries | 2025 5th International Conference on Advanced Research in Computing: Converging Horizons: Uniting Disciplines in Computing Research through AI Innovation, ICARC 2025 - Proceedings | |
| dc.subject | anomaly detection | |
| dc.subject | cattle behavior | |
| dc.subject | IoT | |
| dc.subject | machine learning | |
| dc.subject | YOLOv8 | |
| dc.title | Bovitrack:Animal behavior monitoring using Machine learning and IoT | |
| dc.type | Article |
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