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

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.