2024-2

Nordic-Baltic Plant Phenotyping Network (NB-PPN)

Coordinator: Svend Christensen,  University of Copenhagen,

 

Photo: Sahameh Shafiee, NMBU

Remote sensing and spectral analysis are revolutionizing many aspects of human society especially in agricultural systems. Crop phenotyping by remote sensing and spectral analysis is rapidly developing and is becoming increasingly relevant for agriculture and plant breeding. There is a clear need to keep both academic and private stakeholders informed about the development and to share experiences and knowledge. That is what we wanted to do with a focus on the Nordic and Baltic countries, which paved the way for the establishment of the Nordic-Baltic Plant Phenotyping Network (NB-PPN).

The primary goal of the new NB-PPN is to strengthen the plant phenotyping community in the Nordic-Baltic region. We do this by joining two previous networks together, NPPN and NordPlant, and continue to build on their strengths. This will be done by maintaining the annual symposia and other network activities (see goals above).

Additionally, the NB-PPN will have special development initiatives aimed at expanding the open innovation of plant phenotyping among the partners. We would like to create better connections between breeding companies and tech companies in the region. In the period from 2024 to 2025, NB-PPN will particularly focus on the competence development of all network members in the areas of data management and AI.

 

Background: This network comes from two previous projects; The Nordic Public-Private Partnership Plant Phenotyping Project (6P project), funded under the Nordic Public-Private Partnership program, and the NordPlant project, supported by NordForsk, both concluded at the end of 2023.
In both projects, we have conducted research and developed methodologies for phenotyping crop plants to advance plant science and breeding plants with greater resilience to climate change through advanced sensing, data analysis, and modeling. We have developed fundamental research and research protocols and demonstrated platforms for interpreting collected sensor data and testing novel trait combinations. These achievements have been made possible through the combined expertise of academia, technology companies, and plant breeders.

The main accomplishments of the two projects include:

  • Development of protocols for indoor and field phenotyping using various sensing systems.
  • Creation of digital tools and software for managing sensor data.
  • Advancement in statistical techniques and the utilization of machine learning.
  • Exploration of physiological interpretations of data obtained from various sensing systems.
  • Initiation of mechanistic modeling involving genotype, environment, and management interactions.


Project partners:
• Lantmännen
• DLF
• Danespo
• Graminor
• Sejet
• University of Copenhagen/Dep of Plant and Environmental Sciences (PLEN)
• Swedish University of Agricultural Sciences (SLU)
• Norwegian University of Life Sciences (NMBU)
• Findus
• The Agricultural University of Iceland (LBHI)
• Natural Resources Institute Finland (LUKE)
• Tystoftefonden
• Estonia Crop Research Institute (ECRI)
• LAMMC Lithuanian Research Centre for Agriculture and Forestry
• University of Helsinki
• UiT The Arctic University of Norway
• Lund University
• Norwegian Institute of Bioeconomy Research (NIBIO)

 

Contact:
Katja Annette Willrodt, network facilitator,