Shelemia Nyamuryekung'e
Research Scientist
(+47) 477 64 707
shelemia.nyamuryekunge@nibio.no
Place
Tjøtta
Visiting address
Parkveien, 8861 Tjøtta
Authors
Zachary Winkler Laura E. Boucheron Santiago Utsumi Shelemia Nyamuryekung'e Matthew McIntosh Richard E. EstellAbstract
Body condition score (BCS) has been a useful tool in estimating the health of cattle for many years now. This categorical metric requires experienced observers to visually inspect cows and assess body fat deposits regularly via a time consuming, subjective process. Low cost RGB+depth cameras have been used alongside machine learning algorithms in the past and have shown great promise, however, more advanced techniques are projected to yield better performance. In this work, a vision transformer (ViT) is pretrained using a recently developed self-supervised pretraining method, masked image modeling, and then fine-tuned on RGB+depth BCS data with the objective of improving performance. Model accuracy was found to be highly dependent on dataset curation, ranging from 64% to 92% accuracy. These discrepancies are attributed to non-unique data in the training and test splits and an inherently unbalanced dataset, both of which are discussed in detail. It is recommended that engineers and animal scientists collaborate more closely, as certain details related to dataset curation are critical to thoroughly assess performance and robustness of automated methods for BCS determination.
Abstract
No abstract has been registered
Authors
Shelemia Nyamuryekung'eAbstract
On the Ground: -Precision livestock management through sensor technology using the Internet of Things offers enhanced surveillance and monitoring of the ranching operations. -At the ranch scale, the integration of sensor technology, including on-animal sensors, environmental monitoring equipment, and remote sensing can shift livestock operations from a solely reactive, traditional, knowledge-based approach toward a proactive, data-driven, decision-making process. -Leveraging data from sensors at the ranch scale can address logistical challenges and create efficiency in decision-making processes concerning resource management.