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Low-cost fibre optic matting for direct live-mapping of livestock weight to improve feed efficiency. Development, demonstration & imaging integration.

低成本光纤垫，用于直接实时绘制牲畜体重，以提高饲料效率。

基本信息

批准号：
NE/P007945/1
负责人：
Bruce Grieve
金额：
$ 25.74万
依托单位：
University of Manchester
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2016
资助国家：
英国
起止时间：
2016 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=NE%2FP007945%2F1
关键词：
Low cost fibre optic matting

项目摘要

This project exploits prior art and know-how in sensor systems design at the University of Manchester (UoM), which has previously been utilised for mapping human gait and tested for the ability to estimate weight. This will be translated into a reference pig unit, at SRUC, to prove that the technology can be made suitably robust for weighing pigs on a mat and that the subsequent data may then be integrated with commercial and prototype pig imaging hardware, as developed by SRUC and commercial partners, including estimation of pig weight. The Bristol Robotics Laboratory (BRL) contributes the capability to determine if vision-based biometrics, combined with the tomographic footfall 'signatures' or imaging system, could feasibly be developed to autonomously identify individual pigs and assign weights to each of them, without using Radio Frequency Identification (RFID) tags, which requires a separate scanner unit. Furthermore, surface tags are prone to damage by the animals, whereas subcutaneous tags are not viable due to migration under the skin, rendering them both unreadable and acting as a source of meat contamination.We aim to demonstrate the feasibility of one or more pig weighing methods suitable for the pig farm environment. To achieve that, we'll design and test for robustness a smart mat system enclosure and electronics; we'll deploy and test an optical weighing system; we'll also demonstrate non-contact technology to identify and assign the estimated weight to individual pigs.

该项目利用了曼彻斯特大学（UoM）传感器系统设计的现有技术和专有技术，该技术以前曾用于绘制人类步态图，并测试了估计体重的能力。这将在SRUC转化为参考猪单元，以证明该技术可以适当地用于在垫子上称重猪，并且随后的数据可以与SRUC和商业合作伙伴开发的商业和原型猪成像硬件集成，包括猪体重的估计。布里斯托机器人实验室（BRL）的贡献能力，以确定是否基于视觉的生物识别技术，结合断层扫描的脚步'签名'或成像系统，可以可行地开发自主识别个体猪，并分配重量给他们每一个，而不使用射频识别（RFID）标签，这需要一个单独的扫描仪单元。此外，表面标签容易被动物损坏，而皮下标签由于迁移到皮肤下而无法存活，使它们无法读取并成为肉类污染源。我们的目的是证明一种或多种适用于猪场环境的猪称重方法的可行性。为了实现这一目标，我们将设计和测试智能垫子系统外壳和电子设备的鲁棒性;我们将部署和测试光学称重系统;我们还将展示非接触式技术，以识别和分配估计的重量给每头猪。