Behavioral Monitoring Tool for Pig Farmers: Ear Tag Sensors, Machine Intelligence, and Technology Adoption Roadmap.
Behavioral Monitoring Tool for Pig Farmers: Ear Tag Sensors, Machine Intelligence, and Technology Adoption Roadmap.
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养猪者的行为监控工具：耳号传感器，机器智能和技术采用路线图。
DOI：
10.3390/ani11092665
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发表时间：
2021-09-10
期刊：
影响因子：
--
通讯作者：
Yoon KJ
中科院分区：
文献类型：
--
作者：
Pandey S;Kalwa U;Kong T;Guo B;Gauger PC;Peters DJ;Yoon KJ
在养猪场中，对于农场管理员来说，全天以连续的方式监测所有动物的健康和幸福状态是具有挑战性的。需要自动化工具来远程监控农场中的所有猪，并在需要立即注意的情况下向农场管理员提供早期警报。为了实现这个目标，我们开发了一种传感器板，可以安装在猪的耳朵上，以生成有关动物活动，发声和温度的数据。生成的数据将用于开发机器学习模型，以在测试期间对与每只动物相关的行为特征进行分类。一些因素影响的技术采用农场管理员进行了讨论。精确的养猪生产可以受益于自主，非侵入性和负担得起的设备，这些设备可以经常检查猪的健康状况。在这里，我们提出了一个远程监控工具，一些行为指标，可能有助于评估的健康和福利状况，即姿势，步态，发声，和外部温度的客观测量。多参数电子传感器板的特征在于实验室测量和动物试验。讨论了相关的行为健康指标，用于实施机器学习算法和决策支持工具，以检测动物跛行，嗜睡，疼痛，受伤和痛苦。还讨论了技术采用的路线图，沿着挑战和前进的道路。所提出的技术可能导致农场动物的有效管理，有针对性地关注患病动物，节省医疗成本，减少抗生素的使用。
In a pig farm, it is challenging for the farm caretaker to monitor the health and well-being status of all animals in a continuous manner throughout the day. Automated tools are needed to remotely monitor all the pigs on the farm and provide early alerts to the farm caretaker for situations that need immediate attention. With this goal, we developed a sensor board that can be mounted on the ears of individual pigs to generate data on the animal’s activity, vocalization, and temperature. The generated data will be used to develop machine learning models to classify the behavioral traits associated with each animal over a testing period. A number of factors influencing the technology adoption by farm caretakers are also discussed. Precision swine production can benefit from autonomous, noninvasive, and affordable devices that conduct frequent checks on the well-being status of pigs. Here, we present a remote monitoring tool for the objective measurement of some behavioral indicators that may help in assessing the health and welfare status—namely, posture, gait, vocalization, and external temperature. The multiparameter electronic sensor board is characterized by laboratory measurements and by animal tests. Relevant behavioral health indicators are discussed for implementing machine learning algorithms and decision support tools to detect animal lameness, lethargy, pain, injury, and distress. The roadmap for technology adoption is also discussed, along with challenges and the path forward. The presented technology can potentially lead to efficient management of farm animals, targeted focus on sick animals, medical cost savings, and less use of antibiotics.
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影响因子：
4.6
作者：
Legner CM;Tylka GL;Pandey S
通讯作者：
Pandey S
影响因子：
2.3
作者：
Ashwood, Loka;Diamond, Danielle;Thu, Kendall
通讯作者：
Thu, Kendall
影响因子：
3.3
作者：
Goldhawk, C.;Crowe, T.;Schwartzkopf-Genswein, K.
通讯作者：
Schwartzkopf-Genswein, K.
影响因子：
3.7
作者：
Kalwa, Upender;Legner, Christopher;Pandey, Santosh
通讯作者：
Pandey, Santosh
影响因子：
1.2
作者：
Czycholl, I.;Kniese, C.;Krieter, J.
通讯作者：
Krieter, J.