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.

In this project we will demonstrate how coordinating renewable energy availability with energy expenditure enables PACE horticulture facilities to be an asset to the evolving smart energy grid. The lack of fruit and vegetables on supermarket shelves this spring arose from a multitude of factors, including high energy prices discouraging UK growers from planting protected horticultural crops during winter 2021/22. Lighting, heating, and ventilation each contribute to energy bills for growers but lighting can comprise 70% of these costs in indoor farms and light intensity is immediately responsive to energy consumption (in contrast to heating and ventilation which vary over longer time periods). Our ultimate goal is to allow PACE horticulture infrastructure to present itself as a "shiftable load" to the electricity grid. This type of demand flexibility management is often deployed in complex, time-critical industrial processes where power consumption schedules can be varied provided that the final product falls within acceptable tolerances. Demand flexing has significant commercial advantages and will be increasingly important as controllable (fossil fuel) energy generation decreases as a proportion of our electricity supply. Despite the potential advantages of demand flexing for PACE horticulture we still need to determine how crop growth is affected by varied light irradiation. Plants alter their development dependent on prevailing environmental conditions. Varied light regimes consequently produce variation within the crops produced. We can control this 'developmental plasticity' by genetically manipulating the signalling pathways which control plants responses to light. We will assess whether previously generated 'timeless' plants (which we have designed to respond uniformly to light signals) are better able to maintain crop yield, quality, and uniformity when demand flexing is applied. In this project we have three distinct aims; 1) We need to demonstrate that demand flexing is applicable in PACE horticulture so that we can optimise energy usage whilst maximising crop productivity. 2) We need to understand how demand flexing can be integrated with existing flexible light regimes to maximise crop yield and quality. 3) We need to confirm that our genetically engineered 'timeless' plants have uniform performance during demand flexing so that we can maximise crop productivity and achieve Net Zero goals. Objectives We will exploit our understanding of crop photobiology and existing genetic resources to understand how best to apply demand flexing to PACE horticulture. 1) We will assess the growth and biochemical characteristics of crops grown under exemplar demand flexing schemes to demonstrate the utility of this approach. 2) We will assess how demand flexing can be integrated with a varied light regime to maximise crop yield. 3) We will assess the performance of 'timeless' plants in PACE horticulture so that we can maximise crop productivity during the application of demand flexing. Applications and Benefits The positioning of PACE horticulture as flexible assets in the evolving smart electricity grid will have commercial benefits for growers and will enhance the viability of the industry. Increased commercial viability of PACE horticulture will allow the distribution of infrastructure alongside sites of renewable energy generation. This distributed production will have societal benefits beyond those conferred by their produce alone. For instance, a distributed placement of smaller scale indoor farms within communities will reduce food mileage and provide job opportunities within these areas, enabling a Just Transition in energy use.

This project seeks to develop cryogenic hard X-ray microscopy (imaging) with sub-micron spatial resolution in the laboratory. It will explore a range of soft biological samples, including plant and animal tissues, investigating the contrast enhancement provided by freezing. It will assess the efficacy of different freezing methods (e.g., flash vs. ramped) on crystallisation within samples, taking advantage of the increased field of view achievable compared with electron microscopy. Furthermore, since hard X-rays possess greater penetrating power, they enable regions of interest within larger biological volumes to be examined. The biological community considers X-ray photon energies above 6 keV as 'hard'. Lab-based imaging systems typically produce X-rays above 20 keV. Relying upon absorption contrast to differentiate between structures for quantifying morphology, it remains challenging to characterise soft biological materials due to their lower X-ray absorption coefficient. Utilising phase differences generated by X-rays interacting with samples is possible, however, the resulting refractive indexes are very small (compared with optical photons) and it's currently unrealistic to achieve quantitative phase contrast X-ray imaging using laboratory systems, due to incoherent sources and insufficient distance between sample and X-ray detector for phase fringes to propagate for adequate measurement. Chemical staining is the standard method for providing additional absorption contrast of soft biological materials. Lugol's iodine and heavy metals (osmium, cobalt) are commonly used. Still, for soft biological tissues long X-ray exposure times are required to generate sufficient contrast in labs, and during these extended scan times soft biological materials can degrade. The stains themselves can also induce shrinkage and warping in tissues. Lab-based hard cryogenic X-ray imaging has synergy with volume electron microscopy, such as focused ion beam SEM, that can produce higher resolution with smaller fields of view, and lab-based soft cryogenic X-ray microscopy, that's currently in development for imaging cells. Correlative workflows using these techniques would allow rich multi-scale volumetric datasets to be obtained. This project requests funding for a PDRA to explore a range of biological soft materials using a novel cryogenic hard X-ray imaging set-up located within the Future Technology Centre at the University of Portsmouth that is capable of providing sub-micron resolution. It will provide researchers and industrialists access to a method within a lab setting, removing an existing bottleneck created by synchrotron access models.