测量个体动物行为、生理参数的传感器技术越来越多用于奶牛场,以提高繁殖能力和健康管理。这些技术在每头牛的水平上产生了大量高分辨率的数据,因此对使用这些数据的兴趣超出了对牛群管理。这项研究属于ICAR的Brian Wickham青年交流计划（BWPEX）,采访ICAR5 个成员组织、研究机构,以深入了解传感器数据超出预期用途的使用带来好处和挑战。本次采访话题有：使用传感器数据的最大潜力,特别是对于受访组织;传感器数据目前在受访组织如何使用,以及计划在未来使用;存在哪些挑战以及如何克服这些挑战;传感器数据如何用于改善动物健康、福利以及育种;传感器数据对未来奶业的重要性。本次访谈合作人员都认为传感器数据在牛群管理之外有巨大潜力,并有兴趣在他们组织中使用它。然而,关于大家已经认同的几个挑战,尽管存在克服这些挑战的想法,但最终得出的结论是,基于传感器数据的第三方应用程序或其他产品的开发尚未准备好。本次采访还提及如何应用这些数据提高动物健康、福利和育种,大家一致认为这些数据未来对奶业发挥重要作用。
Sensor technologies measuring individual animal behaviour and physiological parameters are increasingly used in dairy farms to improve fertility and health management. These technologies produce a large amount of high-resolution data at individual cow level and thus interest in using these data exists beyond herd management. In this study,which was conducted within ICAR’s Brian Wickham Young Persons Exchange Program （BWPEX） ffve representatives from ICAR member organizations and research institutions were interviewed to gain more insights into benefits and challenges of the use of sensor data beyond its intended purpose. The topics addressed in the interview were about below topics. （1）The greatest potential of using sensor data in general and for the interview partner’s organization specifically.（2）How sensor data is currently used in the interview partner’s organization and planned to be used in the future.（3）Which challenges exist and how they can be overcome.（4）How sensor data can be used for animal health and welfare improvement and for breeding.（5） How important sensor data will be for the dairy industry in the future. All interview partners attributed great potential to the use of sensor data beyond herd management and were interested in using it also in their organizations. However, several challenges were identified and although ideas on how to overcome them exist, it was concluded that the development of third-party applications or other products based on sensor data is not ready yet. Some aspects of how the data may contribute to enhancement of animal health and welfare and in a breeding context were mentioned and there was consensus that these data will play an important role for dairy industry in the future.
[1] Yongfeng L,Hang S,Jérôme B,et al.Classification and analysis of multiple cattle unitary behaviors and movements based on machine learning methods[J].Animals,2022,12(9):1060-1060.
[2] S E F,L D S,M G C, et al.Developing a simulated online model that integrates GNSS,accelerometer and weather data to detect parturition events in grazing sheep: A machine learning approach[J].Animals,2021,11(2):303-303.
[3] Lessire F,Moula N,Hornick J L,et al.Systematic review and meta-analysis:identification of factors influencing milking frequency of cows in automatic milking systems combined with grazing[J].Animals:an Open Access Journal from MDPI,2020,10(5):913.
[4] Peng D,Chen S,Li G,et al.Infrared thermography measured body surface temperature and its relationship with rectal temperature in dairy cows under different temperature-humidity indexes[J].International Journal of Biometeorology,2019,63(3):327-336.
[5] Venu K M,Rajalakshmi S,S B,et al. Data security tolerance and portable based energy-efficient framework in sensor networks for smart grid environments[J].Sustainable Energy Technologies and Assessments,2022:52(PB):102184.1-102184.10
[6] Ghazel A Sharifa A.The effectiveness of depth data in liveness face Authentication using 3D sensor cameras.[J].Sensors (Basel, Switzerland),2019,19(8):1928-1928.
[7] Tomas T,Hans N .Temporal calibration and synchronization of robotic total stations for kinematic multi-sensor-systems[J].Journal of Applied Geodesy,2020,15(1):13-30.
[8] John B,Der V M V,Henk H,et al. Forecasting chronic mastitis using automatic milking system sensor data and gradient-boosting classifiers[J].Computers and Electronics in Agriculture,2022,198:1-9.
