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）正在彻底改变农业和农艺。例如，约翰·迪尔（John Deere）是一家近200年历史的农业公司，最近利用自动化和AI [1]改变了其业务。它的收集和管理大量数据的能力是如此之大，以至于该公司现在认为自己是软件公司[2]。使用传感器在现场，温室和/或多孔中收集数据的能力，并通过自动分析对该数据进行作用，具有巨大的潜力。但是，利用这些功能需要目前在大多数英国生物科学家中缺乏的技术实力。可悲的是，在日常生物科学环境中部署了用于实际使用的AI系统的广泛使用能力，但学术界和行业都没有可悲的是，在全国范围内有一个令人信服的命令，可以为生物科学家提供使他们能够实现这种令人兴奋的潜力的技能。在去年的英国AI部门协议中，农业和生命科学被确定为主要投资领域，AI可以在英国经济中提高生产力。但是，如果没有知识渊博和熟练的劳动力，这项倡议注定要失败；如果没有适当的培训，生物科学家将无法将全球农业和生命科学革命带入新的AI驱动解决方案。图像在生物科学中无处不在，并且是客观，定量数据的关键来源。与机器人辅助图像和其他数据捕获的AI合并的最新发展，以及相对较低的成本计算设备的可用性，可以使高通量获取和分析现实世界中的数据分析以及学术研究实验室之外。尽管存在技术设施，但也需要设计和实施它们的实践知识。这与生物科学家特别相关，谁必须回答关键问题才能选择和实施有效的解决方案：如何设计AI驱动方法？它们如何适应生物科学中的新领域？我们如何在我们的实验室或现场研究中利用它们？应考虑的数据集应考虑什么？在没有适当的培训和技能的情况下，生物科学家将无法解决这些问题。因此，数据CAMPP项目提供了一个创新的培训课程，并具有灵活的，动手的学习机会，涵盖了关键生物科学设置的自动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。该课程将提供涵盖图像分析，机器学习和应用于植物表型的数据处理的基本和高级方面的单元。培训单元伴随着可下载的软件工具，练习和数据集，以及新颖的“实验室”项目套件（物理硬件和植物），以通过远程参与来实现动手学习体验。该课程还将提供互补的面对面活动。这种独特的混合交付方式可促进广泛的队列的可及性，以支持来自一系列教育背景和技能的参与者，在多元化的职业阶段，以及各种个人约束，可能会限制旅行和/或正常的白天出勤率。数据的总体目标是，数据CAMPP的总体目标是为生物学家社区创建独特的和及时的学习经验，以覆盖生物学家的构图，并在Rob的范围内构建现场，并在Rob的范围内构建了现场，并设置了现场的现场，该领域的现场构成了现场，并在现场上建立了现场，该领域的现场既有现场又有范围的现场，该领域是在现实中的景点机器学习的实验和道德实践。 Data Campp将准备当今的生物科学家领导明天的AI驱动创新。[1] www.deere.co.uk/en/agriculture/future-farming [2]