A cost-sensitive reinforcement learning environment shows an agent can learn when to pay for crop measurements to guide nitrogen fertilization in winter wheat.
CropGym: a Reinforcement Learning Environment for Crop Management
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abstract
Nitrogen fertilizers have a detrimental effect on the environment, which can be reduced by optimizing fertilizer management strategies. We implement an OpenAI Gym environment where a reinforcement learning agent can learn fertilization management policies using process-based crop growth models and identify policies with reduced environmental impact. In our environment, an agent trained with the Proximal Policy Optimization algorithm is more successful at reducing environmental impacts than the other baseline agents we present.
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To Measure or Not: A Cost-Sensitive, Selective Measuring Environment for Agricultural Management Decisions with Reinforcement Learning
A cost-sensitive reinforcement learning environment shows an agent can learn when to pay for crop measurements to guide nitrogen fertilization in winter wheat.