ArticleTranslational animal science2024
Online feeding behavior monitoring of individual group-housed grow-finish pigs using a low-frequency RFID electronic feeding system.
Article in Translational animal science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Video-Based Feeding Demand Sensing and Offline Feeding Strategy Evaluation in Group-Housed Pigs: A Single-Pen Proof-of-Concept Study Using Oriented Object Detection.Animals : an open access journal from MDPI · 2026Article
- Characterising responses in group-housed pigs to Salmonella typhimurium infection through integrated computer vision-based behavioural monitoring and statistical analyses.Porcine health management · 2026Article
- The Piggy Solution: Harnessing Food Waste for Sustainable Hog Farming.Global challenges (Hoboken, NJ) · 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
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Authors and funding
4 authors.
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Abstract
Early identification of animals in need of management intervention is critical to maximize animal health and welfare and minimize issues with productivity. Feeding behavior, captured by automated feeding systems, can be used to monitor the health and welfare status of individual pigs. Here, we present a framework for monitoring feeding behavior of grow-finish pigs in real time, using a low-frequency radio frequency identification (RFID) system. Using historical data, an autoregressive linear model for predicting daily time at the feeder was developed and utilized to detect anomalous decreases in feeding behavior associated with health status of the pig. A total of 2,826 pigs were individually monitored with our warning system over the entire grow-finish period, and health warnings were compared to caretaker diagnoses. The system detected 55.7% of the caretaker diagnoses, and on average these events were detected 2.8 d earlier than diagnosis by the caretaker. High numbers of potentially spurious health warnings, generated by the system, can be partly explained by the lack of a reliable and repeatable gold standard reference data set. Results from this work provide a solid basis for monitoring individual animals, but further improvements to the system are necessary for practical implementation.
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