Application note: Autonomous operation mode identification of agricultural machinery with large language models

Leveraging extensive trajectory data to analyze the operation modes of agricultural machinery for gathering precise spatial information is an important fundamental task for subsequent agricultural machinery trajectory research. However, complex algorithm models hinder nonspecialized researchers from further processing agricultural machinery trajectory data. In the present application note, ChatGPT is taken as an example and a complete prompt guide for large language models (LLMs) is provided for autonomously identifying the operation mode of agricultural machinery. This guide provides low-cost workflows for processing agricultural machinery trajectory data when computer science or data science expertise is lacking. It even possesses the capability to utilize newly learned algorithms such as the random forest model, which has not been previously explored in the literature for operation mode identification, to accomplish the task. To the best of our knowledge, this is the first attempt to apply LLMs to identifying agricultural machinery operation mode based on trajectory data. The complete prompt guide is publicly available at https://github.com/kakushuu/prompt-guide/. Keywords: operation mode identification, large language models, ChatGPT, prompt guide, agricultural machinery trajectory DOI: 10.25165/j.ijabe.20251805.9082 Citation: Zhai W X, Guo Z, Han R J, Xu Z, Cheng X Y, Song S H, et al. Application note: Autonomous operation mode identification of agricultural machinery with large language models. Int J Agric & Biol Eng, 2025; 18(5): 215–222.