UK Plant Phenomics Town Hall and Conference Report 2023

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.