ArticleAnimals : an open access journal from MDPI2023
Analysis of the Drinking Behavior of Beef Cattle Using Computer Vision.
Article in Animals : an open access journal from MDPI, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Computer Vision for Cattle Health and Welfare Monitoring: A Comprehensive Review of Methods, Applications, and Interdisciplinary Integration in Smart Agriculture.Sensors (Basel, Switzerland) · 2026Review
- Early prediction of final body weight in Hanwoo steers using machine and deep learning models.Animal bioscience · 2026Article
- ASAS-NANP Symposium: Mathematical modeling in animal nutrition: revolutionizing animal farming with artificial intelligence: trends, challenges, and opportunities.Journal of animal science · 2026Review
- Developing and Assessing the Performance of a Machine Learning Model for Analyzing Drinking Behaviors in Minipigs for Experimental Research.Sensors (Basel, Switzerland) · 2026Article
- Integrating Deep Learning and Transcriptomics to Assess Livestock Aggression: A Scoping Review.Biology · 2025Review
- Revolutionizing pig farming: Japan's technological innovations and environmental strategies for sustainability.Journal of advanced veterinary and animal research · 2025Article
- Transformation toward precision large-scale operations for sustainable farming: A review based on China's pig industry.Journal of advanced veterinary and animal research · 2024Review
- Computer vision algorithms to help decision-making in cattle production.Animal frontiers : the review magazine of animal agriculture · 2024Article
- Programming and Setting Up the Object Detection Algorithm YOLO to Determine Feeding Activities of Beef Cattle: A Comparison between YOLOv8m and YOLOv10m.Animals : an open access journal from MDPI · 2024Article
- Field Implementation of Precision Livestock Farming: Selected Proceedings from the 2nd U.S. Precision Livestock Farming Conference.Animals : an open access journal from MDPI · 2024Article
- Dynamic Serpentine Convolution with Attention Mechanism Enhancement for Beef Cattle Behavior Recognition.Animals : an open access journal from MDPI · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.