An Intelligence Cattle Reidentification System Over Transport by Siamese Neural Networks and YOLO

Abstract

Livestock, throughout their lifespan, are transported to multiple destinations before being processed into consumable goods. The assurance of authentic product delivery hinges on the presence of a reliable, intelligent identification system. However, extant livestock identification methodologies, primarily relying on radio frequency identification (RFID) ear tags, are vulnerable to loss, failure, and cases of misidentification or improper substitution. This paper introduces an Artificial Intelligence (AI)-enabled system to rectify these issues by leveraging deep learning facial recognition for cattle re-identification. It utilises an integrated approach combining the Only Look Once version 5 (YOLOv5) algorithm for cattle face detection and the Siamese Neural Network (SNN) for subsequent recognition. The system was rigorously tested on a prepared dataset consisting of 2,500 cattle face images, demonstrating an impressive accuracy of 95.13% when supplied with a single query image and a 20-image sample per cow from our dataset. This system can be deployed across diverse environments, including farms, cargo areas, and sale yards, without necessitating model re-training. Furthermore, it can be fine-tuned to identify other farm animals, indicating its broad applicability.