An Internet of Things Approach to Vision-Based Livestock Monitoring: PTZ Cameras for Dairy Cow Identification
Year: 2025
Type: article
Abstract: The Internet of Things (IoT) offers promising solutions for smart agriculture, particularly in the monitoring of livestock. This paper proposes a contactless, low-cost system for individual cow identification and monitoring in a dairy barn using a single Pan–Tilt–Zoom (PTZ) camera and a YOLOv8 deep learning model. The PTZ camera periodically scans the barn, capturing images that are processed to detect and recognize a specific target cow among the herd without any wearable sensors. The system embeds barn area metadata in each image, allowing it to estimate the cow’s location and compute the frequency of its presence in predefined zones. We fine-tuned a YOLOv8 object detection model to distinguish the target cow, achieving high precision in identification. Experimental results in a real barn environment demonstrate that the system can identify an individual cow with 85.96% Precision and 68.06% Recall, and the derived spatial occupancy patterns closely match ground truth observations. Compared to conventional methods requiring multiple fixed cameras or RFID-based wearables, the proposed approach significantly reduces equipment costs and animal handling stress. It should be noted that the present work serves as a proof-of-concept for targeted cow tracking that identifies and follows a specific individual within a herd rather than a fully generalized multi-cow identification system.
Source: Telecom
Authors: Niken Prasasti Martono, Ryota Tsukamoto, Hayato Ohwada
Institution: Tokyo University of Science
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $1,448
OpenAlex ID: W4415823590