Digging Deeper with AI: Canada-UK-US Partnership for Next-generation Plant Root Anatomy Segmentation

利用人工智能进行更深入的挖掘：加拿大、英国、美国合作开发下一代植物根部解剖分割

• 批准号: BB/Y513908/1
• 负责人: Michael Pound
• 金额: $ 31.34万
• 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: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FY513908%2F1
• 关键词: Digging; Deeper; AI; Canada; UK

For many decades plant phenotyping has been used to help us understand the biological mechanisms that underpin plant growth and health. Measuring plants lets us seek out new crops that are higher yielding, or more resilient in the face of a changing climate or evolving diseases. Roots, the unseen and often overlooked part of plants, play a pivotal role in the development of strong and robust crops. Root systems extract water and nutrients from the surrounding soil, and a high performing root system can transform the performance of the plant above ground. There has been a great deal of research on the automatic measurement of root architectures - the arrangement of root systems in soil or substrate. Teams including those at the University of Nottingham, UK, the University of Saskatchewan, CA, and the Donald Danforth Plant Science Centre, USA, have developed techniques to acquire images of 2D and 3D root architecture and computer vision and AI software to measure these images quickly and automatically.The study of root anatomy - the organisation of cells within a root - has proven a more challenging task1. Microscopy and other similar images of roots are often very high resolution, and there may be many thousands of cells within even a small area. Many existing solutions have focused on 2D segmentation, but like root architecture, root anatomy is an inherently 3D challenge. Our ability to understand the biological mechanisms and benefits of root anatomy will always be limited until we can reliably and quickly phenotype these dense tissue structures.This project will push forward the technology that underpins high-resolution segmentation of 3D root anatomy by leveraging the imaging facilities at Nottingham, and the world-leading plant phenotyping and AI expertise at Nottingham, Saskatchewan, and the Donald Danforth Plant Science Centre. Nottingham houses modern imaging facilities at the Hounsfield Facility: a Laser Ablation Tomograph (LAT), and new micro-computed X-Ray tomography (µCT) platforms that collect 3D data at high throughput and resolution. Nottingham has also undertaken important work in 2D segmentation of root anatomy, which will provide a foundation for the 3D segmentation methods developed here. Researchers at the University of Saskatchewan are experts in working with large datasets, using AI to detect objects in 2D, and objects and events in video sequences. Their expertise will allow us to identify important biological features as we traverse through the 3D stack, combining these features with the existing 2D segmentations into a detailed 3D map of the root tissue. Researchers at the Donald Danforth Plant Science Center have expertise in plant phenotyping and 3D imaging, and low-cost devices. Their image data captured on the same species as those at Nottingham will provide important cross-platform image variability, letting us train generalisable models that work for the whole community. By working on common crop varieties important to the economies of the UK, Canada and the US, the AI solutions will be more general and more robust than those developed by a single lab working alone.Gaining a better understanding root anatomy will drive forward bioscience research, letting us better understand how root adaptations affect water and nutrient uptake. All trained models and the final segmentations will be shared with our partners in North America and released into the wider research community.

几十年来，植物表型分析一直被用来帮助我们了解支持植物生长和健康的生物机制，通过测量植物，我们可以寻找产量更高、或者在面对气候变化或根部疾病时更具抵抗力的新作物。根系是植物中看不见且经常被忽视的部分，在作物的生长过程中发挥着关键作用，它可以从周围的土壤中吸收水分和养分，而高性能的根系可以改变植物在地面上的性能。已经发生了很多事包括英国诺丁汉大学、加利福尼亚州萨斯喀彻温大学和美国唐纳德·丹福斯植物科学中心在内的团队已经开发了自动测量根系结构（根系在土壤或基质中的排列）的研究。获取 2D 和 3D 根结构图像的技术以及计算机视觉和人工智能软件来快速自动测量这些图像。根解剖学（根内细胞的组织）的研究已证明是一项更具挑战性的任务1。的根部的分辨率通常非常高，即使在很小的区域内也可能有数千个细胞。许多现有的解决方案都集中在 2D 分割上，但与根部结构一样，根部解剖学本质上是我们理解生物机制的能力的挑战。在我们能够可靠、快速地对这些致密组织结构进行表型分析之前，根解剖学的好处将始终受到限制。该项目将利用诺丁汉和世界各地的成像设施，推动支持 3D 根解剖结构高分辨率分割的技术。龙头厂诺丁汉、萨斯喀彻温省和唐纳德·丹福斯植物科学中心的表型和人工智能专业知识在亨斯菲尔德设施中拥有现代化的成像设施：激光烧蚀断层扫描仪 (LAT) 和新的微计算机 X 射线断层扫描 (μCT) 平台，可收集数据。高通量和分辨率的 3D 数据还在根解剖学的 2D 分割方面开展了重要工作，这将为 3D 提供基础。萨斯喀彻温大学的研究人员是处理大型数据集的专家，他们使用人工智能来检测二维对象以及视频序列中的对象和事件，他们的专业知识将使我们能够在遍历时识别重要的生物特征。 3D 堆栈，将这些特征与现有的 2D 分割结合成根组织的详细 3D 地图。唐纳德丹福斯植物科学中心的研究人员拥有植物表型和 3D 成像以及低成本设备方面的专业知识。他们在诺丁汉捕获的同一物种的图像数据将提供重要的跨平台图像变异性，使我们能够通过研究对英国、加拿大和其他国家经济重要的常见作物品种来训练适用于整个社区的通用模型。在美国，人工智能解决方案将比单个实验室单独开发的解决方案更通用、更强大。更好地了解根部解剖结构将推动生物科学研究，让我们更好地了解根部适应如何影响水和养分吸收。训练好的模型和最终的分割将是与我们在北美的合作伙伴分享并发布到更广泛的研究社区。