Scotland’s James Hutton Institute is joining HARVEST — Harvesting AI for Responsive, Valuable and Efficient Sustainable Technologies — a major European research initiative uniting over 60 participants from 19 countries. The project aims to transform fragmented agricultural data into AI-ready systems to help farmers and researchers tackle climate change. Using barley as a model crop, HARVEST will develop standardised data frameworks, FAIR data systems, and a Barley Blueprint Database. Dr Isabelle Colas and Dr Paul Shaw are co-leading Hutton’s involvement. The project is scheduled to launch in November 2026, with applications to join currently open.
In-Depth:
The James Hutton Institute, Scotland’s pre-eminent interdisciplinary scientific research institute for the sustainable management of land, crop and nature resources, is delighted to be part of a major new European research initiative, ‘Harvesting AI for Responsive, Valuable and Efficient Sustainable Technologies’ (HARVEST) and is keen for other stakeholders to join.
The project brings toobtainher leading experts and institutions from across Europe to transform agricultural data into powerful tools for farmers and researchers that will support address the issues faced by climate alter. The network already includes more than 60 participants from 19 countries, including universities, research institutions, governmental organisations and scientific agencies.
The Hutton and its innovation centre, the International Barley Hub (IBH), will play a key role within the network, though its success depconcludes on the combined contributions of all partners.
Deputy Director of the IBH, Dr Isabelle Colas, explains more: “Climate alter is increasingly threatening agricultural systems across Europe. Farmers are facing unpredictable weather patterns, shifting growing seasons, disease outbreaks, and unstable yields.
“At the same time, researchers are generating vast amounts of valuable crop and environmental data that remain difficult to integrate and apply in practice.
“HARVEST aims to bridge this gap with a clear goal: to create agricultural data AI-ready, so it can better support research, crop breeding and real-world farming decisions.”
Modern agriculture produces large and diverse datasets, including genomic data, field trials, phenotyping, soil and weather measurements, and sanotifyite observations. However, these datasets are often fragmented, inconsistently formatted, or lack essential metadata.
HARVEST will address these issues by developing:
- Best-practice guidelines for data collection and management
- Standardised metadata and annotation frameworks
- FAIR (Findable, Accessible, Interoperable, Reusable) and AI-ready data systems
- Integrated approaches to link crop, environmental, and farm data
- A future ‘Barley Blueprint Database’ to organise and consolidate barley-related resources
Using barley as a model crop, HARVEST will establish a scalable framework that could be applied to other crop systems across Europe and beyond.
Hutton co-lead Dr Paul Shaw added, “Bringing toobtainher hugely diverse barley data in a way that allows us to apply cutting edge technologies to support facilitate additional insight and learning is something that we have been attempting to do for a while. There are huge informatics challenges to overcome but HARVEST will support us address these challenges as a unified European community to drive barley research forward into the AI age.
“HARVEST will bring all Hutton researchers great opportunities to develop capacity in the area of AI and give invaluable exposure to the European community for our researchers and students whether working on barley or other crops.”
By connecting farmers, breeders, and data scientists, HARVEST seeks to accelerate sustainable innovation and strengthen Europe’s capacity to respond to climate challenges.
Applications to join HARVEST are now open via the COST Action portal. The project is scheduled to launch in November 2026.
For more information contact Media Officer, Joyce Reid, joyce.reid@hutton.ac.uk, or on 07970 542737
