Reinforcement Learning for Nitrogen Fertilization Recommendations

Duration: 0.5 hour

Participants: –

This presentation showcases student work on a crop management use case, leveraging Reinforcement Learning (RL) and the cropgym package. Students trained an AI agent to optimize fertilization strategies for winter wheat. The RL environment, powered by the Lintul3 crop growth model, simulates plant growth, nitrogen requirements, and environmental leaching. This practical application demonstrates the potential of RL in addressing real-world agricultural challenges.

Presentation:

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Project Coordination

Dr. Spyros Fountas

Associate Professor

Project Communication

Grigoris Chatzikostas

RFF Partner

Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.