PhenoRob builds networks with relevant players worldwide in the field of digital technologies for sustainable crop production. The most important ones are listed below in alphabetical order. Furthermore, PhenoRob is one of the founding members of DigiCrop.Net. DigiCrop.Net is an association of excellent research groups and institutions who support a technology-driven approach as one way to address the challenges and investigate novel approaches for achieving sustainable crop production.

Research Institutions

AI Institute for Next Generation Food Systems (AIFS): The AI Institute for Next Generation Food Systems draws together researchers from six institutions, including UC Davis, University of California, Berkeley, Cornell University, the University of Illinois at Urbana-Champaign, the United States Department of Agriculture, and University of California Agriculture and Natural Resources.

AI Institute for Resilient Agriculture (AIIRA): The AI Institute for Resilient Agriculture is a collaborative project involving faculty members from eight universities and organizations across the United States.

Artificial Intelligence for Future Agricultural Resilience, Management, and Sustainability (AIFARMS): The Artificial Intelligence for Future Agricultural Resilience, Management, and Sustainability Institute project is led by the Center for Digital Agriculture (CDA) at the University of Illinois at Urbana-Champaign.

ETH Zurich: At the Swiss Federal Institute of Technology in Zurich, the Cluster collaborates mainly with the Department of Environmental Systems Science (D-USYS) and the Department of Civil, Environmental and Geomatic Engineering (D-BAUG).

University of Lincoln: At the University of Lincoln, the Cluster collaborates with the School of Computer Science, the University’s cross-disciplinary research center in Robotics, the Lincoln Centre for Autonomous Systems (L-CAS), and the Lincoln Agri-Robotics Centre (LAR).

The University of Melbourne: At the University of Melbourne, the Cluster collaborates with the Faculty of Science, in particular with the Adrienne Clarke Chair of Botany.

Queensland University of Technology: At QUT, the Cluster collaborates with the Faculty of Engineering, more specifically with researchers from the School of Electrical Engineering and Robotics.

Wageningen University & Research (WU&R): At Wageningen University & Research, the Cluster collaborates with the Graduate School for Production Ecology & Resource Conservation (PE&RC) and several Departments. The Faculty of Agriculture at the University of Bonn has a joint PhD program with WU&R.


Lamarr Institute for Machine Learning and Artificial Intelligence: The Lamarr Institute for Machine Learning and Artificial Intelligence is one of five German AI competence centers. As a new international AI center of excellence the Lamarr Institute focuses on the value-based research and development of high-performance, trustworthy as well as resource-efficient Artificial Intelligence (AI). It connects pioneering research institutions in Germany: the Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS) in Sankt Augustin, the Fraunhofer Institute for Material Flow and Logistics (IML) in Dortmund, the Technical University of Dortmund, and the University of Bonn.

The University of Bonn is one of eleven co-applicants of the FAIRagro consortium. The FAIRagro consortium includes different members from the agrosystem sciences of Germany. It is a community-driven initiative that is part of the National Research Data Infrastructure (NFDI). Its aim is to advance agrosystem research through collaborative Research Data Management to enable and support linkages across disciplines, scales and methods. FAIRagro's focus is the agrosystem domain that is needed to develop sustainable crop production and agroecosystems. The goal is findable, accessible, interoperable and reusable research data. PhenoRob is involved in a use case focusing on noninvasive phenotyping with autonomous robots. This use case showcases the potential of multimodal data analytics methods and machine-learning algorithms for in-field plant phenotyping.

Farmwissen: is a platform that deals with the digitization in agriculture. It serves the transfer of knowledge in that it provides digital recipes to try out, explained step-by-step in the practical examples, to digitally advance a farm. The possibilities of farm-specific digital development are presented in a cooperation of digital experimentation fields.

International Plant Phenotyping Network (IPPN): The International Plant Phenotyping Network is an association representing the major plant phenotyping centers. IPPN aims to provide all relevant information about plant phenotyping. The goal is to increase the visibility and impact of plant phenotyping and enable cooperation by fostering communication between stakeholders in academia, industry, government, and the general public. Through workshops and symposia, IPPN seeks to create different working groups and distribute all relevant information about plant phenotyping in a web-based platform.

NRW-Agrar research network (Forschungsnetzwerk NRW-Agrar): The NRW-Agrar research network was founded in 2006 with the aim of intensifying cooperation in agricultural research. The research network comprises an overarching strategy platform, subject-specific information platforms and joint research projects. PhenoRob is part of the research network through the Faculty of Agriculture of the University of Bonn.

Plattform Land- und Ernährungswirtschaft im Rheinischen Revier (PLAIN-RR): The Platform Agriculture and Food Industry in the Rhenish Mining District has the goal to jointly shape the future in the mining district, develop the region for the next 20-30 years, and drive structural change. The platform includes the regions’ most important players: the Rheinische Landwirtschafts-Verband RLV, the Landwirtschaftskammer NRW, the NRW-Agrar research network, the Food-Processing Initiative, the University of Bonn, the Forschungszentrum Jülich, the RWTH Aachen, and the Kompetenznetzwerk Umweltwirtschaft.