Respiratory diseases in pigs impact the wellbeing of animals and increase the cost of production. One of the most appropriate approaches to minimizing these negative effects is the early detection of ill animals. The use of cameras coupled with computer-based techniques could assist the early detection of physiological changes in pigs when they are beginning to become ill and prior to exhibiting clinical signs. This study consisted of two experiments that aimed to (a) evaluate the use of computer-based techniques over RGB (red, green, and blue) and thermal infrared imagery to measure heart rate and respiration rate of pigs, and (b) to investigate whether eye-temperature, heart rate and respiration rate assessed remotely could be used to identify early signs of respiratory diseases in free-moving, and group-housed growing pigs in a commercial piggery. In the first experiment, the remotely-obtained heart rate and respiration rate were compared with the measures obtained with standard methods, showing positive correlations (r = 0.61 – 0.66; p < 0.05). In the second experiment, pigs were recorded by overhead cameras and the remotely-obtained physiological measures were analysed to identify whether physiological changes could be detected in sick pigs before clinical signs were observed. The changes in eye-temperature and heart rate remotely obtained showed clear differences between sick and healthy pigs two days before clinical signs were detected. While significant changes in respiration rate occurred the day before clinical signs of illness were identified. The results of the present study indicate the possible use of computer vision technique for constant animal monitoring and rapid detection of physiological changes related to illness in commercial pigs. Further research is recommended to continue the development, automatization, and commercial practicality of this novel technology.

猪的呼吸道疾病会影响动物的健康并增加生产成本。减少这些负面影响的最合适的方法之一是及早发现患病动物。使用相机和基于计算机的技术可以帮助在猪开始生病和表现出临床症状之前及早发现它们的生理变化。本研究包括两个实验，旨在 (a) 评估基于计算机的技术在 RGB（红色、绿色和蓝色）和热红外图像上的使用，以测量猪的心率和呼吸率，以及 (b) 调查远程评估的眼温、心率和呼吸率是否可用于识别自由活动中呼吸系统疾病的早期迹象，和在商业猪舍中群养生长猪。在第一个实验中，远程获得的心率和呼吸率与使用标准方法获得的测量值进行比较，显示出正相关（r = 0.61 – 0.66；p < 0.05）。在第二个实验中，通过头顶摄像机记录猪，并分析远程获得的生理测量，以确定是否可以在观察到临床症状之前检测到病猪的生理变化。在检测到临床症状前两天，远程获得的眼温和心率的变化表明病猪和健康猪之间存在明显差异。而呼吸率的显着变化发生在确定疾病临床症状的前一天。本研究的结果表明，计算机视觉技术可能用于持续动物监测和快速检测与商品猪疾病相关的生理变化。建议进一步研究以继续这种新技术的开发、自动化和商业实用性。