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

arXiv: https://arxiv.org/abs/2501.12823 · HTML: https://mlsanity.com/paper/2501.12823/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 10 of its 49 references that are in the map.
- First posted on arXiv 22 Jan 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 10 of 10)

- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Quantifying Generalization in Reinforcement Learning](https://mlsanity.com/paper/1812.02341/) (2018)
- [Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models](https://mlsanity.com/paper/1905.05824/) (2019)
- [Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power](https://mlsanity.com/paper/2205.13863/) (2022)
- [Reinforcement Learning with Efficient Active Feature Acquisition](https://mlsanity.com/paper/2011.00825/) (2020)
- [CropGym: a Reinforcement Learning Environment for Crop Management](https://mlsanity.com/paper/2104.04326/) (2021)
- [gym-DSSAT: a crop model turned into a Reinforcement Learning environment](https://mlsanity.com/paper/2207.03270/) (2022)
- [A SWAT-based Reinforcement Learning Framework for Crop Management](https://mlsanity.com/paper/2302.04988/) (2023)
- [Dynamic Observation Policies in Observation Cost-Sensitive Reinforcement Learning](https://mlsanity.com/paper/2307.02620/) (2023)
- [Current applications and potential future directions of reinforcement learning-based Digital Twins in agriculture](https://mlsanity.com/paper/2406.08854/) (2024)

## Built on by (most cited first, 2 of 2)

- [Cohort-Based Active Modality Acquisition](https://mlsanity.com/paper/2505.16791/) (2025)
- [Optimizing Algorithms for Mobile Health Interventions with Active Querying Optimization](https://mlsanity.com/paper/2512.08950/) (2025)

## Abstract (arXiv)

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.