Contact
Dr. Abel Barreto
Further information
Duration:
09/2025 – 08/2028
Project team:
Dr. Abel Barreto,
Hendrik Könning,
Dr. Stefan Paulus
Department:
Funding:
Federal Ministry of Agriculture, Food and Regional Identity (BMLEH), BMEL – Digitalization in agriculture (28DE401A23)
Cooperations:
Strube D&S GmbH, KWS Saat SE & Co. KGaA, Pfeifer & Langen GmbH & Co, BASF SE, Ecorobotix ARA, Farming Revolution GmbH, Most Robotics GmbH, Agvolution GmbH, Fraunhofer-Institut für Graphische Datenverarbeitung (IGD), Julius Kühn-Institut, PhenoRob2 - Robotik und Phänotypisierung für Nachhaltige Nutzpflanzenproduktion, ZAZIkI
The FarmerSpaceAI project aims to evaluate, further develop, and clearly communicate AI-based digital technologies in plant protection under practical conditions. The goal is to support more precise, resource-efficient, and environmentally sustainable crop production. The project focuses on innovative approaches to weed control, precise and site-specific crop treatment, and the use of specialized chatbots as digital decision-support tools. A particular emphasis is placed on ensuring that the developed solutions can be transferred to both organic and conventional farming systems.
Within the framework of the project, the following outcomes are pursued:
Development, implementation, and quantitative evaluation of an LLM (Large Language Model) prototype for digital decision support, including practical validation and assessment of its scalability toward multimodal LLM systems.
Establishment of a practice-oriented testing concept for AI-based precision applicators, including systematic method comparison, concrete advisory recommendations, and evaluation of transfer potential between conventional and organic weed control.
Testing of a concept for needs-based, small-scale crop treatment, including practice-oriented recommendations and evaluation of variable rate application (VRA) and site-specific application techniques.
Assessment of the robustness, transparency, and traceability of the AI systems used through a structured overview of models and datasets, validated data collection, and transparent documentation of decision-making logic.
Strengthening knowledge transfer through the provision of open-access datasets, scientific and practice-oriented publications, and intensified exchange between academia, industry, and agricultural practice.
Förderung
Funded by the Federal Ministry of Agriculture, Food and Reginal Identity by decision of the German Bundestag, and by the Federal Office for Agriculture and Food as project manager.
Project partners
Georg-August-Universität Göttingen, Department für Nutzpflanzenwissenschaften, Abteilung Agrartechnik
Fraunhofer-Institut Optronik, Systemtechnik und Bildauswertung IOSB
Landwirtschaftskammer Niedersachsen
hessian.AI - The Hessian Center for Artificial Intelligence
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