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Using imagery and computer vision as remote monitoring methods for early detection of respiratory disease in pigs

呼吸频率 心率 呼吸速率 生命体征 呼吸 医学 血压 外科 内科学 解剖
作者
Maria Jorquera-Chavez,Sigfredo Fuentes,Frank R. Dunshea,R. D. Warner,Tomas Poblete,Ranjith Rajasekharan Unnithan,R. S. Morrison,Ellen C. Jongman
出处
期刊:Computers and Electronics in Agriculture [Elsevier BV]
卷期号:187: 106283-106283 被引量:17
标识
DOI:10.1016/j.compag.2021.106283
摘要

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.
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