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

Data CAMPP（植物表型数据采集、分析和管理创新培训）

• 批准号: MR/V038850/1
• 负责人: Andrew French
• 金额: $ 115.27万
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FV038850%2F1
• 关键词: Data; CAMPP; Innovative; Training; Capture

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

人工智能(AI)正在给农业和农学带来革命性的变化。例如，John Deere是一家拥有近200年历史的农业公司，最近利用自动化和人工智能[1]实现了业务转型。它收集和管理海量数据的能力如此强大，以至于该公司现在认为自己是一家软件公司[2]。使用传感器在野外、温室和/或多层隧道收集数据，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能力，而这是目前大多数英国生物科学家所缺乏的。遗憾的是，学术界和产业界都缺乏广泛使用、甚至开发展示这些功能的人工智能系统的能力，这些系统被部署在日常生物科学环境中进行实际使用。然而，全国都迫切需要为生物科学家提供使他们能够实现这一令人兴奋的潜力的技能。在去年的英国人工智能行业交易中，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率的关键投资领域。但如果没有知识和技能的劳动力，这一倡议注定会失败；如果没有适当的培训，生物科学家将无法领导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。人工智能领域的最新发展-与机器人辅助的图像和其他数据捕获相结合，以及占地面积小、成本相对较低的计算设备的可用性，使得能够在学术研究实验室以外的真实世界环境中高通量采集和分析数据。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。这与生物科学家尤其相关，他们必须回答关键问题才能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？我们如何在我们的实验室或实地研究中使用它们？应对生成的数据集给予什么考虑？没有适当的培训和技能，生物科学家就无法解决这些问题。因此，Data CAMPP项目提供了一个创新的培训课程，提供了灵活的动手学习机会，涵盖关键生物科学环境的自动化数据收集管道的关键方面。“数据CAMPP”是指数据的自动捕获、分析和管理。本课程将提供图像分析、机器学习和应用于植物表型分析的数据处理的基础和高级方面的单元。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“逐个实验室”项目工具包(实物硬件和设备)，以便通过远程参与获得实践学习体验。该课程还将提供补充的面对面活动。这一独特的混合授课模式促进了广泛人群的可获得性，以支持来自不同教育背景和技能组合、处于不同职业阶段的参与者，以及可能限制旅行和/或定期白天出席的各种个人限制。Data CAMPP的首要目标是为生物科学界创造独特和及时的学习体验，涵盖从机器人技术的开发和在外地的放置，到表型图像集的管理，以及机器学习的良好实验做法和伦理。Data CAMPP将使今天的生物科学家做好准备，领导明天的人工智能驱动的创新。[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