Data CAMPP (Innovative Training in Data Capture, Analysis and Management for Plant Phenotyping)

人工智能（AI）正在彻底改变农业和农艺学。例如，约翰迪尔（John Deere）是一家拥有近200年历史的农业公司，该公司最近进行了业务转型，利用自动化和人工智能技术。它收集和管理大量数据的能力是如此强大，以至于该公司现在认为自己是一家软件公司b[2]。利用传感器收集现场、温室和/或多通道的数据，并通过自动分析对这些数据采取行动的能力显示出巨大的潜力。然而，利用这些能力需要技术实力，这是目前大多数英国生物科学家所缺乏的。遗憾的是，学术界和工业界都缺乏广泛使用和开发具有这些功能的人工智能系统，并将其用于日常生物科学环境的实际应用。然而，国家迫切需要向生物科学家提供技能，使他们能够实现这一令人兴奋的潜力。在去年的英国人工智能产业协议中，农业和生命科学被确定为人工智能可以提高英国经济生产力的关键投资领域。但是，如果无法获得知识渊博和熟练的劳动力，这一倡议注定要失败；如果无法获得适当的培训，生物科学家将无法引领全球农业和生命科学革命，走向新的人工智能驱动的解决方案。图像在生物科学中无处不在，是客观定量数据的关键来源。人工智能的最新发展与机器人辅助图像和其他数据捕获相结合，以及占地面积小、相对低成本的计算设备的可用性，使得在学术研究实验室之外的现实环境中实现高通量数据采集和分析。虽然有技术设施，但也需要设计和实施这些设施的实用知识。这对生物科学家来说尤其重要，他们必须回答关键问题，以便选择和实施有效的解决方案：人工智能驱动的方法是如何设计的？它们如何适应生物科学的新领域？我们如何在实验室或实地研究中利用它们？对得到的数据集应该考虑什么？如果没有适当的培训和技能，生物科学家就没有能力解决这些问题。因此，Data CAMPP项目提供了一个创新的培训课程，具有灵活的实践学习机会，涵盖了关键生物科学设置的自动化数据收集管道的关键方面。“Data camp”指的是数据的自动捕获、分析和管理。本课程将涵盖图像分析、机器学习和数据处理应用于植物表型的基础和高级方面。培训单元配有可下载的软件工具、练习和数据集，以及新颖的“逐岗实验室”项目工具包（物理硬件和设备），以便通过远程参与实现实践学习体验。该课程还将提供补充的面对面活动。这种独特的混合交付模式促进了广泛队列的可访问性，以支持来自不同教育背景和技能组合的参与者，在不同的职业阶段，以及可能限制旅行和/或常规日间出席的各种个人限制。Data camp的总体目标是为生物科学界创造一个独特而及时的学习体验，涵盖从机器人技术在该领域的开发和部署，到表型图像集的管理，以及机器学习的良好实验实践和伦理等主题。Data camp将帮助当今的生物科学家为引领未来的人工智能创新做好准备。[1] www.deere.co.uk/en/agriculture/future-of-farming[2] spectrum.ieee.org/view-from-the-valley/robotics/artificial-intelligence/want-a-really-hard-machine-learning-problem-try-agriculture-say-john-deere-labs-leaders

Artificial Intelligence (AI) is revolutionising agriculture and agronomy. As an example, John Deere is a near 200-year-old agriculture company which has recently transformed its business, capitalizing on automation and AI [1]. So great is its capability to collect and manage huge quantities of data that the firm now considers itself a software company [2]. The ability to use sensors for collecting data in the field, glasshouse and/or polytunnel, and to act on that data via automated analysis, shows huge potential. However, taking advantage of these capabilities requires technical prowess that is currently lacking in the majority of UK bioscientists. The widespread ability to use and, indeed, develop AI systems exhibiting these functionalities, deployed for practical use in day-to-day bioscience settings, is sadly absent from both academia and industry.Yet there is a compelling imperative nationally to provide bioscientists with the skills that enable them to realise this exciting potential. In last year's UK AI Sector Deal, agriculture and life sciences was identified as a key investment area where AI can boost productivity in the UK economy. But without access to a knowledgeable and skilled workforce, this initiative is doomed to fail; and without access to appropriate training, bioscientists will be unable to lead the global agriculture and life science revolution toward new AI-driven solutions.Images are ubiquitous in the biosciences and are a key source of objective, quantitative data. Recent developments in AI-combined with robot-assisted image and other data capture, as well as the availability of small-footprint, relatively low-cost computing devices enable high-throughput acquisition and analysis of data in real-world settings, beyond academic research labs. While the technical facilities exist, the practical knowledge to design and implement them is also required. This is particularly relevant for bioscientists, who must answer key questions in order to select and implement effective solutions: How are AI-driven methods designed? How can they be adapted to new domains in the biosciences? How can we utilise them in our lab or field research? What consideration should be given to the resulting datasets? Without appropriate training and skills, bioscientists are ill-equipped to address these questions.The Data CAMPP project, therefore, provides an innovative training course with flexible, hands-on learning opportunities spanning key aspects of an automated data gathering pipeline for the critical bioscience setting. "Data CAMPP" refers to the automated Capture, Analysis and Management of data. The course will deliver units covering fundamental and advanced aspects of image analysis, machine learning and data handling applied to Plant Phenotyping. Training units are accompanied by downloadable software tools, exercises and datasets, and novel "lab-by-post" project kits (physical hardware and plants) to enable hands-on learning experiences via remote participation. The course will also offer complementary in-person activities. This unique mode of mixed delivery promotes accessibility for a broad cohort, to support participants from a range of education backgrounds and skill sets, at diverse career stages, and with varied personal constraints that might limit travel and/or regular daytime attendance.The overarching goal of Data CAMPP is to create a unique and timely learning experience for the bioscience community, covering topics from development and placement of robotics in the field, through to management of phenotyping image sets, and good experimental practices for, and ethics of, machine learning. Data CAMPP will prepare today's bioscientists to lead tomorrow's AI-driven innovations.[1] www.deere.co.uk/en/agriculture/future-of-farming[2] spectrum.ieee.org/view-from-the-valley/robotics/artificial-intelligence/want-a-really-hard-machine-learning-problem-try-agriculture-say-john-deere-labs-leaders