The New Agronomists: Language Models are Experts in Crop Management
Jing Wu Zhixin Lai Suiyao Chen Ran Tao Pan Zhao Naira Hovakimyan
Crop management plays a crucial role in determining crop yield, economic
profitability, and environmental sustainability. Despite the availability of
management guidelines, optimizing these practices remains a complex and
multifaceted challenge. In response, previous studies have explored using
reinforcement learning with crop simulators, typically employing simple
neural-network-based reinforcement learning (RL) agents. Building on this
foundation, this paper introduces a more advanced intelligent crop management
system. This system uniquely combines RL, a language model (LM), and crop
simulations facilitated by the Decision Support System for Agrotechnology
Transfer (DSSAT). We utilize deep RL, specifically a deep Q-network, to train
management policies that process numerous state variables from the simulator as
observations. A novel aspect of our approach is the conversion of these state
variables into more informative language, facilitating the language model's
capacity to understand states and explore optimal management practices. The
empirical results reveal that the LM exhibits superior learning capabilities.
Through simulation experiments with maize crops in Florida (US) and Zaragoza
(Spain), the LM not only achieves state-of-the-art performance under various
evaluation metrics but also demonstrates a remarkable improvement of over 49\%
in economic profit, coupled with reduced environmental impact when compared to
baseline methods. Our code is available at
\url{https://github.com/jingwu6/LM_AG}.