Leveraging LLMs for Mission Planning in Precision Agriculture

2025/06/11 by Marcos Abel Zuzuárregui, Zuzuárregui, Marcos Abel, Stefano Carpin +1 · 1 citation
Computer Science · #Advanced Neural Network Applications #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Multimodal Machine Learning Applications #Robotics (cs.RO) #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2506.10093

openalex publication_date 2025/06/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

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Cited by

• One For All: LLM-based Heterogeneous Mission Planning in Precision Agriculture

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