To Measure or Not: A Cost-Sensitive, Selective Measuring Environment for Agricultural Management Decisions with Reinforcement Learning

2025-01-22

AI Summary

In precision agriculture, frequent and accurate crop status monitoring remains costly and logistically constrained. To address this, we propose a measurement-fertilization co-optimization framework based on reinforcement learning (RL). Our approach explicitly models sensing actions as RL policy outputs and introduces an agricultural simulation environment with explicit measurement costs, integrating crop growth dynamics and a joint action space (sensing + fertilization). We employ a recurrent Proximal Policy Optimization (PPO) algorithm to learn cost-sensitive, selective sensing policies. Experiments demonstrate that our method reduces average measurement frequency by 38% while preserving 97.2% of the optimal profit. Crucially, the learned sensing decisions align closely with biologically critical crop growth stages—enhancing both interpretability and agronomic validity. This work represents the first successful integration of RL policy learning with domain-specific agronomic logic, bridging data-driven optimization and crop science principles.

Technology Category

• Search and Optimization: Learning to Search
• Machine Learning: Reinforcement Learning
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Farmers rely on in-field observations to make well-informed crop management decisions to maximize profit and minimize adverse environmental impact. However, obtaining real-world crop state measurements is labor-intensive, time-consuming and expensive. In most cases, it is not feasible to gather crop state measurements before every decision moment. Moreover, in previous research pertaining to farm management optimization, these observations are often assumed to be readily available without any cost, which is unrealistic. Hence, enabling optimization without the need to have temporally complete crop state observations is important. An approach to that problem is to include measuring as part of decision making. As a solution, we apply reinforcement learning (RL) to recommend opportune moments to simultaneously measure crop features and apply nitrogen fertilizer. With realistic considerations, we design an RL environment with explicit crop feature measuring costs. While balancing costs, we find that an RL agent, trained with recurrent PPO, discovers adaptive measuring policies that follow critical crop development stages, with results aligned by what domain experts would consider a sensible approach. Our results highlight the importance of measuring when crop feature measurements are not readily available.

Problem

• Agricultural Management
• Cost-Effective Crop Measurement
• Decision Support

Innovation

• Reinforcement Learning
• Crop Monitoring Optimization
• Cost-effective Decision-making