AI-PigNet: The AI of social interactions for next gen smart animal breeding

人口快速增长、气候变化以及对动物产品需求的增加和动物福利的提高给畜牧业生产带来了前所未有的挑战。自动监测系统和尖端的人工智能（AI）技术为动物与动物之间的相互作用的量化带来了巨大的希望，从而改善了牲畜管理和选择。下一代动物育种可以利用动物相互作用的数据来选择更适合现代生产条件的动物，通过选择行为模式改善和社会适应性更高的动物。为了实现这一目标，有必要克服一些挑战，例如准确监测和量化大量个体中不同类型的社会互动的能力，并将这些措施纳入新的遗传预测模型，以准确预测不同社会环境中动物的生产力和健康的遗传优点。该项目的总体目标是开发新的人工智能程序，从自动化农场监测系统中准确捕获猪的社会互动，并将其整合到智能动物育种的遗传预测模型中。为了实现这一目标，我们将首次联合三个学科，即机器/深度学习（ML/DL），社会网络分析（SNA）和定量遗传学，以实现以下具体目标：人工智能识别社会互动：构建信息社会互动措施，描述社会结构和每个个体在不同类型的假定福利和生产力重要性的社会互动中的作用。人工智能建立社会互动和关键表型之间的联系。确定这些网络和个体社会互动措施如何随着时间的推移而变化，并受动物基因组成的影响，并调查它们与关键生产力、健康和福利特征的关系。人工智能改善预测：将社会互动措施纳入动物生产力、健康和福利的智能育种，并评估预测准确性和遗传增益的改进。为了实现这一目标，我们已经建立了一个多学科的专家团队，包括来自英国和美国领先学术机构的计算遗传学、人工智能、SNA、动物行为和福利以及育种，以及世界上最大的生猪养殖公司PIC，以将知识转化为影响。我们可以访问数千头商品猪的大量独特的多维数据（从第一个成功的自动化系统生成的视频数据，用于在商业条件下可靠地记录动物的位置和姿势，到生产和健康记录以及基因组数据），以建立和验证计算管道，以实现上述项目目标。由于我们强大的背景知识产权、研究记录和可用数据，该泵启动项目专注于猪。然而，我们期望开发的方法将适用于其他生产系统。所产生的方法和知识将有利于不同的利益相关者，例如科学家、育种者和农民。世界领先的生猪养殖公司PIC的直接参与，以及与养殖界的密切联系，将确保该行业迅速吸收研究成果，产生大规模影响。这将有助于提高英国畜牧业科学和工业的竞争力，并产生重大的经济影响。此外，在项目期间组织的互访、培训课程和研讨会使英国和美国合作伙伴之间能够进行技能发展和知识交流，并为智能动物养殖人工智能方面的长期合作建立专业网络。

Rapid population growth, climate change and increasing demand for animal products and higher animal welfare provide unprecedented challenges to livestock production. Automatic monitoring systems and cutting-edge Artificial Intelligence (AI) technologies, hold great promise for the quantification of animal-animal interactions to improve livestock management and selection. Next generation animal breeding can use data from animal interactions to select animals that are better suited for modern production conditions by enabling the selection of animals with improved behavioural patterns and higher social fitness. To accomplish this, it is necessary to overcome several challenges such as the ability to accurately monitor and quantify diverse types of social interactions in large number of individuals and to incorporate these measures into novel genetic prediction models that accurately predict the genetic merit for productivity and health of animals in different social environments.The overall aim of this project is to develop novel AI routines that accurately capture social interactions of pigs from automated on farm monitoring systems and integrate these into genetic prediction models for smart animal breeding. To achieve this, we will for the first time unite three disciplines, i.e. machine/deep learning (ML/DL), social network analysis (SNA) and quantitative genetics, to achieve the following specific objectives:AI to identify social interactions: Construct informative social interaction measures that describe the social structure and the role of each individual for different types of social interactions of putative welfare and productivity importance.AI to establish associations between social interactions and key phenotypes. Establish how these networks and individual social interaction measures change over time and are affected by the genetic make-up of animals, and investigate their association with key productivity, health and welfare traits.AI to improve predictions: Incorporate social interaction measures into smart breeding for animal productivity, health and welfare and evaluate improvement in prediction accuracies and genetic gain.To accomplish this goal, we have established a multi-disciplinary team of experts in computational genetics, AI, SNA, animal behaviour and welfare, and breeding from leading academic institutes in the UK and USA, and PIC, the world's largest pig breeding company to translate knowledge to impact. We have access to a vast amount of unique, multi-dimensional data of thousands of commercial pigs (ranging from video data generated by the first successful automated system for reliably recording animal position and posture under commercial conditions, to production and health records, and genomic data) to establish and validate the computational pipeline to achieve the above project objectives.This pump priming project focuses on pigs due to our strong background IP, research track record and available data. However, we anticipate that the methodology developed will be applicable to other production systems. The methods and knowledge generated will be beneficial for different stakeholders, e.g. scientists, breeders and farmers. Direct involvement of the world leading pig breeding company PIC, and close connections with the farming community will ensure a swift uptake of the research findings by the industry to generate impact at scale. This will help to improve the competitiveness of the UK livestock science and industry, with significant economic impact. Furthermore, exchange visits, and training courses and workshops organized during the life-time of this project allow skill development and knowledge exchange between the UK and USA partners, and establish professional networks for long-term collaborations in AI for smart animal breeding.