Enhanced Animal Behavioural Analytics For Improved Cattle Welfare, Health, Productivity and Sustainability

Quant Foundry人工智能（AI）牲畜监测解决方案与布里斯托大学的布里斯托兽医学院合作，旨在为识别异常牛行为提供世界一流的解决方案，以帮助快速识别不同的疾病。该解决方案将人工智能驱动的动物视频分析结合在自动化农场框架内，以提高健康和福利，降低生产成本和排放。虽然现有许多用于远程监控动物的解决方案，但许多解决方案需要人们的积极参与，但潜在的成本节省很少。其他解决方案需要使用必须由动物穿戴的物理硬件，需要显著的每动物设置和维护成本。国际上有许多正在进行的图像识别研究试验，但很少提到它们用于识别特定疾病。许多应用于牲畜的视频系统不能很好地与动物的数量相匹配，而Quant Foundry系统可以识别和跟踪多个动物，只需很少的计算开销。独特的创新是我们的通用计算模型与标准的现成的硬件，将能够识别每只动物的许多不同的条件。这大大减少了开发、部署和维护的时间和成本。这项研究和可行性研究将在两个领域进行：（i）分类和识别将应用于我们的深度学习算法的关键动物行为特征，以及（ii）商业可行性研究，以评估硬件和识别算法在识别异常行为（如跛行和其他）方面的商业有效性异常运动验证研究将涉及在农业-EPI中心的西南乳品开发中心安装该系统，以记录9个月内挤奶后每头奶牛的连续视频。通过布里斯托大学开发的现有人工智能系统，将识别个体奶牛，并将其与布里斯托兽医学校专家的生产/兽医数据和行为注释相关联，并由外部评估员进行验证。这些数据将用于验证和完善Quant Foundry人工智能解决方案，并将作为该领域的资源进行公众传播。最后阶段将评估原发性跛行、乳腺炎和约翰氏病解决方案的总体有效性，并确定其商业化和研究的益处。这将导致进一步的研究，以推进基本的动物福利，行为和可持续性研究。

The Quant Foundry Artificial Intelligence (AI) Livestock Surveillance Solution in collaboration with Bristol Veterinary School at the University of Bristol aims to provide a world-class solution for the identification of anomalous cattle behaviour to aid in the rapid identification of different ailments. The solution combines AI-driven video analytics of animals within an automated farm framework to increase health and welfare and lower production costs and emissions.While there are a number of existing solutions for remote monitoring of animals, many require an active involvement of people with little potential cost savings. Other solutions require the use of physical hardware that must be worn by the animal, requiring significant per-animal setup and maintenance costs. Internationally there are a number of ongoing research trials for image recognition, however there is little mention of their use identifying specific illnesses. Many video systems applied to livestock do not scale well with the number of animals, whereas the Quant Foundry system can identify and track multiple animals with little computational overhead. The unique innovation is our general computing model with standard off-the-shelf hardware that will be able to identify many different conditions for each animal. This considerably reduces the time and cost of development, deployment and upkeep.This research and feasibility study will be performed across two areas: (i) classification and identification of key animal behaviour features to be applied to our deep learning algorithm, and (ii) a commercial feasibility study to assess the commercial effectiveness of the hardware and identification algorithm for identifying anomalous behaviours such as lameness and other abnormal motions. The validation study will involve an installation of the system at Agri-EPI Centre's South West Dairy Development Centre to record continuous video of every cow after milking over a 9-month period. Through an existing AI system developed by the University of Bristol, individual cows will be identified and linked to production/veterinary data and behavioural annotations from Bristol Vet School experts, verified by external assessors. This data will be used to validate and refine the Quant Foundry AI solution, and will also be curated for public dissemination as a resource for the field.The final stage will be to assess the overall effectiveness of the primary lameness, mastitis and Johne's disease solution and determine its benefits for commercialisation and research. This would lead to further studies to advance fundamental animal welfare, behaviour and sustainability research.