Analysis of the Drinking Behavior of Beef Cattle Using Computer Vision

Monitoring the drinking behavior of animals can provide important information for livestock farming, including the health and well-being of the animals. Measuring drinking time is labor-demanding and, thus, it is still a challenge in most livestock production systems. Computer vision technology usin...

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Bibliographic Details
Published in:Animals (Basel) 2023-09, Vol.13 (18), p.2984
Main Authors: Islam, Md Nafiul, Yoder, Jonathan, Nasiri, Amin, Burns, Robert T., Gan, Hao
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Animal behavior
Artificial intelligence
Automation
Beef cattle
Behavior
Cameras
computer vision
Dairy cattle
Data entry
Deep learning
drinking time
Experiments
Health aspects
Livestock
Livestock farms
Machine vision
Methods
Neural networks
precision livestock farming
Respiration
Sensors
Vision systems
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Summary:Monitoring the drinking behavior of animals can provide important information for livestock farming, including the health and well-being of the animals. Measuring drinking time is labor-demanding and, thus, it is still a challenge in most livestock production systems. Computer vision technology using a low-cost camera system can be useful in overcoming this issue. The aim of this research was to develop a computer vision system for monitoring beef cattle drinking behavior. A data acquisition system, including an RGB camera and an ultrasonic sensor, was developed to record beef cattle drinking actions. We developed an algorithm for tracking the beef cattle’s key body parts, such as head–ear–neck position, using a state-of-the-art deep learning architecture DeepLabCut. The extracted key points were analyzed using a long short-term memory (LSTM) model to classify drinking and non-drinking periods. A total of 70 videos were used to train and test the model and 8 videos were used for validation purposes. During the testing, the model achieved 97.35% accuracy. The results of this study will guide us to meet immediate needs and expand farmers’ capability in monitoring animal health and well-being by identifying drinking behavior.
ISSN:2076-2615
2076-2615
DOI:10.3390/ani13182984