Automated dairy cow identification and feeding behaviour analysis using a computer vision model based on YOLOv8
Monitoring changes in the feeding behaviour of dairy cows is essential for assessing their feeding preferences, milk production, and health status. Sick cows often exhibit altered feeding patterns, such as reduced feeding time and frequency, making early detection crucial for effective farm manageme...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-12-01
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| Series: | Smart Agricultural Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525005350 |
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| Summary: | Monitoring changes in the feeding behaviour of dairy cows is essential for assessing their feeding preferences, milk production, and health status. Sick cows often exhibit altered feeding patterns, such as reduced feeding time and frequency, making early detection crucial for effective farm management. Traditional methods for monitoring feeding behaviour are labour-intensive, time-consuming, and prone to errors. To address these challenges, precision livestock farming technologies have gained increasing attention. While wearable sensors, such as accelerometers and RFID tags, provide accurate data, they have limitations, including high costs and potential stress on animals. Alternatively, computer vision-based approaches offer a non-invasive and efficient solution for monitoring feeding behaviour. Deep learning techniques, particularly the YOLO (You Only Look Once) object detection model, have been widely applied in animal husbandry. Despite advancements in object detection, individual cow recognition in operational environment remains a challenge due to the lack of a standardized and viable approach.The main aim of the paper is to evaluate the reliability and validate a deep learning-based computer vision model for automatically recognizing individual cows at the feeding lane in a relevant environment. By identifying individual cows, it is possible to determine their feeding time, feeding duration and daily frequency. The paper describes the work phases from data collection to analysis and validation of an improved YOLOv8n model that, after a fine-tuning on the collected video set, achieved a precision of 85 %, a recall of 62 % (F1 score 0.72) at IoU 0.5 and processes a 640 × 640 pixels frame in just 12 ms on an NVIDIA RTX 2080. The promising results presented here contribute to the advancement and validation of computer vision applications in herd monitoring, supporting the commercial adoption of these technologies for analysing cow behaviour so increasing animal welfare and the sustainability of the animal production. |
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| ISSN: | 2772-3755 |