ORIGINAL RESEARCH article
Front. Anim. Sci.
Sec. Animal Welfare and Policy
Volume 6 - 2025 | doi: 10.3389/fanim.2025.1673750
This article is part of the Research TopicAdvancing Animal Reproduction: AI, Precision Technologies and Reproductive BiotechnologiesView all 6 articles
Automated activity Analysis of Pregnant, Pre-partum, and Post-partum Dromedary female Camels Using YOLOv8 and SAMURAI Tracking
Provisionally accepted
- 1Fujairah Research Centre, Al Hilal, United Arab Emirates
- 2Mohamed bin Zayed University of Artificial Intelligence, Masdar City, United Arab Emirates
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Introduction: Behavioral monitoring during reproductive stages is vital for camel welfare and effective farm management. This study aimed to assess daily activity patterns in pregnant, prepartum, and postpartum dromedary camels using AI-based video analysis tools. Methods: Two experiments were conducted under farm conditions in Fujairah, UAE. In Experiment 1, YOLOv8 with SAMURAI tracking analyzed short 15-minute video segments from 25 pregnant camels at selected times. In Experiment 2, 12 camels with colored neck collars were continuously monitored during late pregnancy, parturition, and postpartum. Results: In Experiment 1, camels exhibited clear circadian rhythms, with increased sitting and sleeping activities at night, and heightened eating, drinking, and standing/walking during daylight hours. In Experiment 2, significant behavioral shifts were observed across reproductive states. Prepartum camels displayed elevated standing durations (699.7 ± 45.7 min/day) and reduced eating (128.3 ± 48.2 min/day), indicating prepartum restlessness. Postpartum camels gradually regained normal activity levels, with increased sitting (722.3 ± 65.4 min/day), sleeping (194.0 ± 15.4 min/day), and eating (229.1 ± 20.9 min/day) within the first 24 hours after calving. Discussion: These findings validate the use of the YOLOv8–SAMURAI system and long-term collar-based identification as reliable, non-invasive tools for automated camel activity assessment. The observed activity markers provide meaningful indicators for reproductive status and recovery, enabling early detection of health or welfare issues. This research supports the development of precision management systems for camels in intensive farming environments.
Keywords: Dromedarycamels, Artificialintelligence, YOLOv8–SAMURAItracking, Reproductivemanagement, welfare
Received: 26 Jul 2025; Accepted: 15 Oct 2025.
Copyright: © 2025 Mansour, Alhashmi, Cholakkal, Nasef and Lamghari. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Nabil Mansour, nabil.mansour@frc.ae
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