# Leveraging LLMs for Mission Planning in Precision Agriculture

arXiv: https://arxiv.org/abs/2506.10093 · HTML: https://mlsanity.com/paper/2506.10093/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 30 references that are in the map.
- First posted on arXiv 11 Jun 2025.

## Builds on (most cited first, 17 of 17)

- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [Code as Policies: Language Model Programs for Embodied Control](https://mlsanity.com/paper/2209.07753/) (2022)
- [Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents](https://mlsanity.com/paper/2201.07207/) (2022)
- [DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model](https://mlsanity.com/paper/2310.01412/) (2023)
- [Integrated Task and Motion Planning](https://mlsanity.com/paper/2010.01083/) (2020)
- [Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving](https://mlsanity.com/paper/2310.01957/) (2023)
- [SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models](https://mlsanity.com/paper/2309.10062/) (2023)
- [Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models](https://mlsanity.com/paper/2408.02442/) (2024)
- [Semantic Anomaly Detection with Large Language Models](https://mlsanity.com/paper/2305.11307/) (2023)
- [Advancing Spatial Reasoning in Large Language Models: An In-Depth Evaluation and Enhancement Using the StepGame Benchmark](https://mlsanity.com/paper/2401.03991/) (2024)
- [ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning](https://mlsanity.com/paper/2406.19741/) (2024)
- [Multi-robot Mission Planning in Dynamic Semantic Environments](https://mlsanity.com/paper/2209.06323/) (2022)
- [Toward Automated Programming for Robotic Assembly Using ChatGPT](https://mlsanity.com/paper/2405.08216/) (2024)
- [Solving Stochastic Orienteering Problems with Chance Constraints Using Monte Carlo Tree Search](https://mlsanity.com/paper/2409.03170/) (2024)
- [Resilient Temporal Logic Planning in the Presence of Robot Failures](https://mlsanity.com/paper/2305.05485/) (2023)
- [Improving the ROS 2 Navigation Stack with Real-Time Local Costmap Updates for Agricultural Applications](https://mlsanity.com/paper/2407.18535/) (2024)
- [Solving Stochastic Orienteering Problems with Chance Constraints Using a GNN Powered Monte Carlo Tree Search](https://mlsanity.com/paper/2409.04653/) (2024)

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

- [N(CO)2: Neural Combinatorial Optimization with Chance Constraints to Solve Stochastic Orienteering](https://mlsanity.com/paper/2606.18514/) (2026)
- [As You Wish: Mission Planning with Formal Verification using LLMs in Precision Agriculture](https://mlsanity.com/paper/2606.18519/) (2026)

## Abstract (arXiv)

Robotics and artificial intelligence hold significant potential for advancing precision agriculture. While robotic systems have been successfully deployed for various tasks, adapting them to perform diverse missions remains challenging, particularly because end users often lack technical expertise. In this paper, we present an end-to-end system that leverages large language models (LLMs), specifically ChatGPT, to enable users to assign complex data collection tasks to autonomous robots using natural language instructions. To enhance reusability, mission plans are encoded using an existing IEEE task specification standard, and are executed on robots via ROS2 nodes that bridge high-level mission descriptions with existing ROS libraries. Through extensive experiments, we highlight the strengths and limitations of LLMs in this context, particularly regarding spatial reasoning and solving complex routing challenges, and show how our proposed implementation overcomes them.