An automated monitoring system for phenotypic characteristics of Reindeer (Rangifer tarandus) using AutoML technologies

Contemporary automated monitoring systems in animal husbandry and environmental protection rely on advanced computer vision methods to evaluate animal traits. This research introduces an automated animal condition monitoring system using the YOLOv11 convolutional neural network. The system was tested on specialized datasets featuring images of deer alongside their pre-measured attributes. Through the AutoGenNet software platform, processes such as hyperparameter tuning and architecture configuration were automated, streamlining and accelerating the adaptation of the model for monitoring various animal species. The findings highlight the effectiveness of YOLOv11 for this application. Additionally, the study validates AutoGenNet’s role in automating the development of models for monitoring reindeer phenotypic characteristics, supporting the integration of AI systems in modern animal husbandry. The biometric data obtained allow for the calculation of derivative metrics—like live weight, muscle mass, and reproductive potential—that inform management decisions. A significant accomplishment is the deployment of contactless monitoring, which removes stress linked to animal handling and adheres to bioethical standards. Successful trials on reindeer, which pose a complex biological challenge due to high variability in characteristics, ensure the method’s applicability to other livestock (such as pigs and sheep) under less stringent conditions. The developed automated monitoring system for reindeer phenotypic characteristics, based on YOLOv11 and AutoGenNet, has proven both technologically feasible and practically valuable.

1. Sobolevsky V.A., Laishev K.A. Automatic animal monitoring systems based on AutoML technologies. Legal Regulation in Veterinary Medicine. 2024;(3):114–116. (In Russ.) https://doi.org/10.52419/issn2782-6252.2024.3.114

2. Chelotti J.O., Martinez-Rau L.S., Ferrero M., et al. Livestock feeding behaviour: A review on automated systems for ruminant monitoring. Biosystems Engineering. 2024;246:150–177. 177. https://doi.org/10.1016/j.biosystemseng.2024.08.003

3. Chen T., Zheng H., Chen J., et al. Novel intelligent grazing strategy based on remote sensing, herd perception and UAVs monitoring. Computers and Electronics in Agriculture. 2024;219:108807. https://doi.org/10.1016/j.compag.2024.108807

4. Das M., Ferreira G., Chen C.P.J. Evaluating model generalization for cow detection in free-stall barn settings: Insights from the COw LOcalization (COLO) dataset. Smart Agricultural Technology. 2025;11:101054. https://doi.org/10.1016/j.atech.2025.101054

5. Djedidi M., Hassen M.B., Mrad H., Koubaa A. Real time contaminants detection in wood panel manufacturing process using YOLO algorithms. Procedia Computer Science. 2025; 253:1226–1235. https://doi.org/10.1016/j.procs.2025.01.184

6. Hu S., Tang G., Yu K., et al. Embedded YOLO v8: Realtime detection of sugarcane nodes in complex natural environments by rapid structural pruning method. Measurement. 2025; 242:116291. https://doi.org/10.1016/j.measurement.2024.116291

7. Leliveld L.M.C., Brandolese C., Grotto M., et al. Realtime automatic integrated monitoring of barn environment and dairy cattle behaviour: Technical implementation and evaluation on three commercial farms. Computers and Electronics in Agriculture. 2024;216:108499.

8. Nandal P., Mann P., Bohra N., et al. Tropical cyclone intensity estimation based on YOLO-NAS using satellite images in real time. Alexandria Engineering Journal. 2025;113:227– 241. https://doi.org/10.1016/j.aej.2024.10.072

9. Redmon J., Divvala S., Girshick R., Farhadi A. You only look once: Unified, real-time object detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA; 2016, pp. 779–788. https://doi.org/10.1109/CVPR.2016.91.2016:779-788.

10. Ren S., He K., Girshick R., Sun J. Faster R-CNN: Towards real-time object detection with region proposal networks. Computer Vision and Pattern Recognition. 2015;arXiv:1506.01497v3 [cs]. https://doi.org/10.48550/arXiv.1506.01497

11. Simonyan K., Zisserman A. Very deep convolutional networks for large-scale image recognition. International Conference on Learning Representations 2015. arXiv:1409.1556 [cs.CV]. https://doi.org/10.48550/arXiv.1409.1556

12. Sobolevskii V. Creation of automatic monitoring systems using AutoML technologies. In: Ilin, I., Youzhong, M. (eds) Digital Systems and Information Technologies in the Energy Sector. Lecture Notes in Networks and Systems. Vol. 1244. Springer, Cham; 2025. https://doi.org/10.1007/978-3-031-80710-7_29

13. Sobolevskii V.A., Kolpaschikov L.A., Rozenfeld S.B., Mikhailov V.V. Monitoring larger vertebrates of the Arctic fauna using intelligent AutoML technology. Zoologičeskij žurnal. 2025; 104(3):112–122. https://doi.org/10.31857/S0044513425030095

14. Xu P., Zhang Y., Ji M., et al. Advanced intelligent monitoring technologies for animals: A survey. Neurocomputing. 2024;585:127640. https://doi.org/10.1016/j.neucom.2024.127640