A Vision-Based Cattle Recognition System Using TensorFlow for Livestock Water Intake Monitoring

This letter presents a new method for identifying individual cattle for the purpose of tracking and efficiently measuring their daily water intake using a vision-based machine learning system. In addition to the current solution of using radio frequency identification (RFID), the proposed system uses TensorFlow Object Detection to detect labels on the RFID tag. The proposed system can be integrated into the current water intake monitoring system and alleviates the errors introduced by the unreliable RFID readers. The system allows users to train an object recognition model that can recognize and differentiate the labels on the ear tags of individual farm animals, so the drinking events can be recorded by the water intake monitoring system. The models are trained using custom datasets of manually annotated tag images with pretrained model architectures from TensorFlow 2 Model Zoo. The system is tested using images from event-triggered weather-proof cameras deployed in the grazing site. Experimental results of the system showed an accuracy of around 90%. In comparison to other present methods, this newly proposed system provides scalability and flexibility, making it an attractive vision-based solution for machine learning systems in agriculture.