We selected two potential providers for hosting the PhenomUK Digital Research Infrastructure. We have been discussing with SCAN and EPCC for several months to present them with the community needs for this scoping project. The EPCC proposal resulted in being considerably more cost-effective. This reduction in costs originates in that we rent resources as part of the EPCC EIDF offering, which also includes cost-effective data storage.

Report mapping the community requirements on Software and Data Authors: Sotirios A Tsaftaris (s.tsaftaris@ed.ac.uk) Valerio Giuffrida (valerio.giuffrida@nottingham.ac.uk) This report aims to provide recommendations of the software provision required to run and support the digital research infrastructure for the PhenomUK community. To have a better idea of the needs of the UK plant research community, we conducted several interviews and collected information on three major aspects: (i) data collection; (ii) software tools; (iii) FAIR data. After several months of gathering insights from researchers via interviews, we found the following points as the current pressing issues the community faces: (i) Manual software pipelines cause bottlenecks in data analysis; (ii) Bespoke code gets forgotten, becoming legacy in a short timeframe; (iii) Data storage is always insufficient as needs grow; (iv) Data curation and sharing is still an issue for many. To address these community needs, we propose a set of recommendations to inform future directions of the PhenomUK Scoping Project.

Foundation models are large models pre-trained on tremendous amount of data. They can be typically adapted to diverse downstream tasks with minimal effort. However, as foundation models are usually pre-trained on images or texts sourced from the Internet, their performance in specialized domains, such as plant phenotyping, comes into question. In addition, fully fine-tuning foundation models is time-consuming and requires high computational power. This paper investigates the efficient adaptation of foundation models for plant phenotyping settings and tasks.

Computers have revolutionised the planet in many ways. Plant science is one of the fields to have benefitted from recent advances in technology, thanks to computer scientists such as Professor Tony Pridmore at the university of Nottingham, UK, who are using their computing skills to address challenges in plant biology. tony is the director of PhenomUK, the UK’s plant phenotyping network, where new software and imaging techniques developed by computer scientists and engineers, in collaboration with plant scientists, could improve crop yields around the world.

1st Call for Funding: Dr Bo Li – NIAB Investigation of microwave imaging for internal fruit quality and below-ground phenotyping Total Fund Requested – £24,783

1st Call for Funding: Prof Bruce Grieve – Electronic Engineering, University of Manchester Implementation of Active 3D Multispectral Imaging and Photometric Stereo systems as Early Stage Phenotyping Tool for Morphological Features and Biotic Stress Quantification within Complementary Demonstrator Phenotyping Centres (IBERS & P3) Total Fund Requested: £18,480

1st Call for Funding: Dr Ji Zhou – NIAB Developing 2D-3D fusion to enable high-throughput phenotypic analysis of key yield-related traits of bread wheat using cost-effective UAV imagery. Total Fund Requested: £24,940

1st Call for Funding: Dr Wenhao Zhang – Centre for Machine Vision, Bristol Robotics Laboratory, UWE, Bristol. An intelligent, low-cost adaptive 3D multi-scale imaging system for advanced plant phenotyping (PS-Plant+) Total Fund Requested – £24,881

2nd Call for Funding: John Ferguson – University of Nottingham. Project Title Leveraging optical topography for rapid reconstructions of 3D leaf surfaces to address key questions in plant biology. Total Fund Requested – £25,000.00

2nd Call for Funding: Dr Matt Jones – Institute of Molecular, Cell, and Systems Biology University of Glasgow Project Title: Measuring the consequences of abiotic stress in vivo using fluorescent and bioluminescent probes Total Fund Requested – £23 687

2nd Call for Funding: Eric Ober – NIAB Project Title In-field 3D imaging for high resolution morphometric phenotyping in wheat Total Fund Requested –£24,772

2nd Call for Funding: Dr. Michael Pound – Computer Vision Laboratory, School of Computer Science, University of Nottingham Project Title Predicting plant root growth from time-series data using deep learning Total Fund Requested – £24,677.34

3rd Funding Call: Yonghuai Liu, Edge Hill University Project Title DeepEarNet: Accurate Segmentation and Measurement of Cereal Grain Spikes Directly in Point Clouds Using Latest Deep Learning and Domain Knowledge. Total Fund Requested – £24,639

Project Title: Seeing the unseeable –Scaling Electrical Impedance Tomography (EIT) to enable a whole plant, high-throughput, phenotyping demonstrator asset for the Phenom-UK community. Total fund Requested £20,000