Livestock Identification using Deep Learning for Traceability
Published in Sensors, 2022
Recommended citation: Dac Hai Ho, Claudia Gonzalez Viejo, Nir Lipovetzky, Eden Tongson, Frank R. Dunshea, and Sigfredo Fuentes. 2022. "Livestock Identification Using Deep Learning for Traceability" Sensors 22, no. 21: 8256. https://doi.org/10.3390/s22218256
This study aimed to develop a face recognition system for dairy farm cows using advanced deep learning models and computer vision techniques. This approach is non-invasive and potentially applicable to other farm animals of importance for identification and welfare assessment. All deep learning models were finetuned through transfer learning on a dairy cow dataset collected from a robotic dairy farm located in the Dookie campus belonging to The University of Melbourne, Australia. Results showed that the accuracy achieved across videos from 89 different dairy cows achieved an overall accuracy of 84%. The computer program developed may be deployed on edge devices, and it was tested on NVIDIA Jetson Nano board with a camera stream. Furthermore, it could be integrated into welfare assessment previously developed by our research group.