CROPS: A Deployable Crop Management System Over All Possible State Availabilities

TL;DR

CROPS is a robust, deployable crop management system utilizing a language model-based reinforcement learning agent that optimizes nitrogen and irrigation strategies, demonstrating state-of-the-art results and adaptability across diverse real-world agricultural scenarios.

Contribution

This paper introduces CROPS, a novel RL-based crop management system that infers masked states and optimizes strategies without pre-defined states or multi-stage training, enhancing robustness and deployability.

Findings

Achieved SOTA results in maize crop management metrics.

Policies are immediately deployable in over ten million real-world contexts.

Policies exhibit noise resilience, reducing sensor bias effects.

Abstract

Exploring the optimal management strategy for nitrogen and irrigation has a significant impact on crop yield, economic profit, and the environment. To tackle this optimization challenge, this paper introduces a deployable \textbf{CR}op Management system \textbf{O}ver all \textbf{P}ossible \textbf{S}tate availabilities (CROPS). CROPS employs a language model (LM) as a reinforcement learning (RL) agent to explore optimal management strategies within the Decision Support System for Agrotechnology Transfer (DSSAT) crop simulations. A distinguishing feature of this system is that the states used for decision-making are partially observed through random masking. Consequently, the RL agent is tasked with two primary objectives: optimizing management policies and inferring masked states. This approach significantly enhances the RL agent's robustness and adaptability across various real-world agricultural scenarios. Extensive experiments on maize crops in Florida, USA, and Zaragoza, Spain, validate the effectiveness of CROPS. Not only did CROPS achieve State-of-the-Art (SOTA) results across various evaluation metrics such as production, profit, and sustainability, but the trained management policies are also immediately deployable in over of ten millions of real-world contexts. Furthermore, the pre-trained policies possess a noise resilience property, which enables them to minimize potential sensor biases, ensuring robustness and generalizability. Finally, unlike previous methods, the strength of CROPS lies in its unified and elegant structure, which eliminates the need for pre-defined states or multi-stage training. These advancements highlight the potential of CROPS in revolutionizing agricultural practices.