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

Authors

  • Weixin Zhai 1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China 2. Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
  • Zhou Guo 1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China 2. Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
  • Ruijing Han 1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China 2. Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
  • Zhi Xu 1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China 2. Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
  • Shuhua Song 3. GEOVIS Wisdom Technology Co., Ltd., Qingdao 266100, China
  • Sun-OK Chung 4. Department of Smart Agricultural System, Graduate School, Chungnam National University, Daejeon 34134, Republic of Korea
  • Bingbing Hu 5. SINOMACH Digital Technology Corporation Co., Ltd. Nanjing 210000, China
  • Mamur Mahmadyorzoda Usmon 6. Tajik agrarian University named Shirinsho Shotemur, Dushanbe, 734003, Republic of Tajikistan
  • Aliev Nozim 6. Tajik agrarian University named Shirinsho Shotemur, Dushanbe, 734003, Republic of Tajikistan
  • Jiawen Pan 1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China 2. Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
  • Caicong Wu 1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China 2. Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications, Ministry of Agriculture and Rural Affairs, Beijing 100083, China

DOI:

https://doi.org/10.25165/ijabe.v18i5.9082

Keywords:

operation mode identification, large language models, ChatGPT, prompt guide, agricultural machinery trajectory

Abstract

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.

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Published

2025-10-27

How to Cite

Zhai, W., Guo, Z., Han, R., Xu, Z., Song, S., Chung, S.-O., … Wu, C. (2025). Application note: Autonomous operation mode identification of agricultural machinery with large language models. International Journal of Agricultural and Biological Engineering, 18(5), 215–222. https://doi.org/10.25165/ijabe.v18i5.9082

Issue

Vol. 18 No. 5 (2025): IJABE

Section

Information Technology, Sensors and Control Systems

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