Learning Crop Management by Reinforcement: gym-DSSAT

romain gautron, Emilio J. Padrón, Philippe Preux, julien bigot, Odalric-Ambrym Maillard, Gerrit Hoogenboom, julien teigny

Keywords: reinforcement learning, crop management, simulator, DSSAT

TL;DR: We turn the state-of-the-art DSSAT crop simulator into a gym environment to train RL agents.

Abstract:

We introduce gym-DSSAT, a gym environment for crop
management tasks, that is easy to use for training Rein-
forcement Learning (RL) agents. gym-DSSAT is based on
DSSAT, a state-of-the-art mechanistic crop growth simulator.
We modify DSSAT so that an external software agent can in-
teract with it to control the actions performed in a crop field
during a growing season. The RL environment provides pre-
defined decision problems without having to manipulate the
complex crop simulator. We report encouraging preliminary
results on a use case of nitrogen fertilization for maize. This
work opens up opportunities to explore new sustainable crop
management strategies with RL, and provides RL researchers
with an original set of challenging tasks to investigate.