Integrating Reinforcement Learning and Large Language Models for Crop Production Process Management Optimization and Control through A New Knowledge-Based Deep Learning Paradigm
Dong Chen, Yanbo Huang
TL;DR
This work surveys the integration of reinforcement learning (RL) and large language models (LLMs) for crop production process management, emphasizing data-driven crop-management decision support systems (DSSs) that cope with climate, soil, and market uncertainties. It outlines RL fundamentals (value-based, policy-based, and actor-critic methods) and recent LLM advances, and discusses crop-management simulators (CropGym, CyclesGym, Gym-DSSAT, Farm-Gym) that enable RL experimentation. The paper also advocates offline RL to overcome real-world data and interaction constraints and highlights the multifaceted roles of LLMs as information processors, reward designers, world-model simulators, decision-makers, and policy interpreters. Key takeaways include the potential to improve yield, resource use, and environmental outcomes through integrated RL-LLM frameworks, while recognizing challenges in sample efficiency, reward design, generalization, and real-world deployment that offline RL and enhanced LLM integration can help address.
Abstract
Efficient and sustainable crop production process management is crucial to meet the growing global demand for food, fuel, and feed while minimizing environmental impacts. Traditional crop management practices, often developed through empirical experience, face significant challenges in adapting to the dynamic nature of modern agriculture, which is influenced by factors such as climate change, soil variability, and market conditions. Recently, reinforcement learning (RL) and large language models (LLMs) bring transformative potential, with RL providing adaptive methodologies to learn optimal strategies and LLMs offering vast, superhuman knowledge across agricultural domains, enabling informed, context-specific decision-making. This paper systematically examines how the integration of RL and LLMs into crop management decision support systems (DSSs) can drive advancements in agricultural practice. We explore recent advancements in RL and LLM algorithms, their application within crop management, and the use of crop management simulators to develop these technologies. The convergence of RL and LLMs with crop management DSSs presents new opportunities to optimize agricultural practices through data-driven, adaptive solutions that can address the uncertainties and complexities of crop production. However, this integration also brings challenges, particularly in real-world deployment. We discuss these challenges and propose potential solutions, including the use of offline RL and enhanced LLM integration, to maximize the effectiveness and sustainability of crop management. Our findings emphasize the need for continued research and innovation to unlock the full potential of these advanced tools in transforming agricultural systems into optimal and controllable ones.