Key Objectives

Current Nordic plant science research infrastructures fully rely on siloed data systems, with limited interoperability between institutions. The data silos persist in genomic data from Norway’s arctic crops, Finland’s cereal trials, and Sweden’s cereal and forestry studies, hindering generating cross-species insights. There are still considerable amounts of manual workflows involved. NordPheno will address the siloed research infrastructure gap by establishment of a Nordic Plant Data Hub, which is an ecosystem, where data from institutional research infrastructures is shared in a trustworthy environment. 

• Nordic Plant Data Hub is a federated system linking servers and cloud service providers to offer a transparent and trustworthy environment to scientists, breeders, farmers and other stakeholders.
• Nordic Plant Data Hub co-develops an evolving, long-term dataset component, which supports data-centric AI approaches for crop modelling and allow continual learning from new observations.
• Nordic Plant Data Hub provides a unified web interface and RESTful API, and supports distributed data query, manipulation, analysis, integration and visualization with open-source formats.

AI, IoT, and edge computing are revolutionizing plant phenotyping by enabling faster, more accurate, and scalable data analysis. AI algorithms process images and sensor data to detect growth patterns, stress responses, and key traits with high precision. IoT devices, such as connected cameras and environmental sensors, monitor temperature, light, moisture, and plant behaviour in real time. Edge computing allows this data to be processed locally, providing instant insights and automated actions such as adjusting irrigation or lighting. These technologies are making plant phenotyping more autonomous, efficient, and accessible, accelerating crop research and the development of climate-resilient, high-yield varieties. NordPheno implements AIoT pipeline in crop research.  

• Data collection via RGB, multispectral, hyperspectral, thermal, and chlorophyll-fluorescence imaging combined with environmental telemetry from controlled chambers and field sites.
• Data is processed leveraging edge intelligence for feature extraction, then stored with metadata in cloud or server systems.
• Multimodal AI uses high-performance computing for training and inference, generating insights for crop modeling and breeding optimization.
• Develop digital twins – dynamic, data-driven models of plant systems and field environments. Digital twins will integrate sensor data, AI predictions, and environmental variables to simulate crop growth.

A critical interdisciplinary knowledge gap exists between plant scientists and computer scientists in implementing AI-based plant phenotyping. Bridging this gap requires educating a new generation of researchers proficient in plant science and computer science. Effective collaboration across disciplines will enable advanced crop modeling leveraging AI to accelerate plant breeding. NordPheno will bridge interdisciplinary knowledge gaps through establishing intensive educational activities, including courses, workshops, seminars, mobilities and shared data processing events, to familiarize researchers from both plant science and computer science with each other’s methodologies, tools, and technological languages. To overcome these barriers, NordPheno will implement structured training programs and collaborative platforms to connect experts in computer science, AI, and plant science.

•  Annual intensive courses (one week) rotating across partner sites, each focused on a flagship topic — AI in Phenotyping, Field Phenotyping, Digital Twins of Canopy, Environment & Phenotypes, Data Processing & Management.
• Annual workshops (one day) that pair an early-career cohort with senior researchers on focused methodological topics.
• Annual spring seminars combining scientific exchange with site visits and hands-on demonstrations.
• Mobility programme. Short- and medium-term research stays for PhD students and postdocs, giving them direct access to facilities (NaPPI, SmartFarm, the UiT climate labs, etc.) outside their home institution.
• Public dissemination. Outreach articles in local Nordic languages, infographics, and policy briefings to make the relevance of plant phenotyping to food security and climate adaptation legible to non-specialist audiences.