Intellipig: An automated on-farm pig health monitoring system

Intellipig：自动化农场生猪健康监测系统

• 批准号: 10073790
• 金额: $ 67.34万
• 依托单位: AGSENZE LTD
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10073790
• 关键词: Intellipig; automated; farm; pig; health

Our proposed System is a completely non-intrusive face-based (artificial) intelligent monitoring system that can automatically capture welfare and health data for monitoring and management of pigs.Why? Being able to continuously monitor and assess farm animal health and welfare depends on the deployment of practical, valid, and reliable measurement tools. Current on-farm welfare assessment protocols involve daily spot-checks by staff, and periodic spot-checks by veterinarians or quality assurance inspectors. Assessed welfare parameters are often resource-based concerning provision for basic needs, or animal-based, looking mainly at easily-measurable factors such as physical condition. Most are performed at a group level as individual identification can be difficult. Rarely is animal behaviour recorded and even rarer still are measures that can tell us something about the emotional state of the individual animal.How will the system work? Our innovative approach is to measure an animal's emotional state as well as its body condition using a completely non-intrusive face-based, (artificial) intelligent monitoring system. We have already successfully developed machine learning algorithms that identify individuals using facial biometrics and are able to detect changes in facial expression that indicate whether a pig is stressed or not. We have also developed body condition scores and weight estimation using these techniques. Here we propose to combine all of these capabilities into one face based, non-intrusive animal health and welfare monitoring station for use on commercial farms. By employing these state-of-the-art machine learning techniques, our system will offer the capacity for on-going learning about individual animals, and consequently allow for early detection of altered health/welfare, personalised thresholds for intervention, and tailored treatment approaches. Such individualised data recording can be integrated with other measurable parameters, such as individual food and water intake, treatment history, growth and weight gain, which will allow better optimisation of farm production efficiency.

我们建议的系统是一个完全非侵入性的基于面部的（人工）智能监控系统，可以自动捕获猪的福利和健康数据，用于监控和管理。为什么？能够持续监测和评估农场动物健康和福利取决于部署实用，有效和可靠的测量工具。目前的农场福利评估协议涉及工作人员的日常抽查，以及兽医或质量保证检查员的定期抽查。评估的福利参数往往是以资源为基础的，涉及基本需求的提供，或以动物为基础的，主要着眼于身体状况等容易衡量的因素。大多数是在小组一级进行的，因为个人识别可能很困难。动物的行为很少被记录下来，更难得的是能够告诉我们一些关于动物个体情绪状态的测量方法。我们的创新方法是使用完全非侵入性的基于面部的（人工）智能监测系统来测量动物的情绪状态以及身体状况。我们已经成功开发了机器学习算法，可以使用面部生物识别技术识别个体，并能够检测面部表情的变化，从而表明猪是否有压力。我们还使用这些技术开发了身体状况评分和体重估计。在这里，我们建议将所有这些功能联合收割机整合到一个基于人脸的非侵入性动物健康和福利监测站中，用于商业农场。通过采用这些最先进的机器学习技术，我们的系统将提供持续学习个体动物的能力，从而允许早期检测改变的健康/福利，个性化的干预阈值和定制的治疗方法。这种个性化的数据记录可以与其他可测量的参数相结合，例如个人的食物和水摄入量、治疗历史、生长和体重增加，这将允许更好地优化农业生产效率。