Funk Taran H, Rohrer Gary A, Brown-Brandl Tami M, Keel Brittney N
U.S. Meat Animal Research Center, ARS, USDA, Clay Center, NE 68933, USA.
Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.
Transl Anim Sci. 2024 Apr 6;8:txae051. doi: 10.1093/tas/txae051. eCollection 2024.
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.
早期识别需要管理干预的动物对于最大化动物健康和福利以及最小化生产问题至关重要。自动饲喂系统记录的采食行为可用于监测个体猪的健康和福利状况。在此，我们提出了一个使用低频射频识别（RFID）系统实时监测生长育肥猪采食行为的框架。利用历史数据，开发了一个用于预测每日在采食器处停留时间的自回归线性模型，并将其用于检测与猪健康状况相关的采食行为异常减少情况。在整个生长育肥期，共有2826头猪通过我们的预警系统进行了个体监测，并将健康预警与饲养员的诊断结果进行了比较。该系统检测到了饲养员诊断结果的55.7%，平均而言，这些事件比饲养员诊断提前2.8天被检测到。该系统产生的大量潜在虚假健康预警，部分原因可能是缺乏可靠且可重复的金标准参考数据集。这项工作的结果为监测个体动物提供了坚实基础，但要实际应用该系统还需要进一步改进。
