Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)

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Amigo-Agri: A Human-Following Robotic Platform with Speech Recognition and Retrieval-Augmented Generation for Smart Farming

Authors
Md. Moniruzzaman Hemal1, 2, Atiqur Rahman1, 3, *, Md. Abdul Halim Khan1, 4, Sadikur Rahman Sadik1, 3, Md. Shohanur Rahman Shohan1, Tahzib Mahmud Rifat1, Md. Ashiqussalehin1, Md. Toukir Ahmed1
1Department of IoT and Robotics Engineering (IRE), University of Frontier Technology, Kaliakair, Gazipur, 1750, Bangladesh
2Department of Computing and Information System, Daffodil International University, Dhaka, 1216, Bangladesh
3Department of Computer Science and Engineering, Daffodil International University, Dhaka, 1216, Bangladesh
4Department of Computer Science and Engineering, Northern University of Business and Technology, Khulna, 9100, Bangladesh
*Corresponding author. Email: 1801025@iot.uftb.ac.bd
Corresponding Author
Atiqur Rahman
Available Online 8 June 2026.
DOI
10.2991/978-94-6239-664-7_58How to use a DOI?
Keywords
Large Language Models (LLMs); Retrieval-Augmented Generation (RAG); Speech Recognition; Smart Farming; Human–Robot Interaction
Abstract

Bridging the gap between physical farm assistance and expert agronomic advice remains an unsolved challenge. We present Amigo-Agri, an integrated platform combining a low-cost, human-following mobile robot with a retrieval-augmented voice assistant. The system splits responsibilities: a smartphone handles speech recognition and knowledge retrieval (RAG), while an Arduino-based robot executes real-time navigation. We address the unique constraints of low-literacy, outdoor environments by enabling hands-free natural language queries alongside motion commands. Experimental results demonstrate a 3.5 s average speechto-speech latency, 0.85 semantic relevance, and robust person-following accuracy (RMSE 0.14 m) in mock field settings. While optical sensing and network dependence remain limitations, this work provides a validated blueprint for affordable, interactive agricultural support.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)
Series
Advances in Intelligent Systems Research
Publication Date
8 June 2026
ISBN
978-94-6239-664-7
ISSN
1951-6851
DOI
10.2991/978-94-6239-664-7_58How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Md. Moniruzzaman Hemal
AU  - Atiqur Rahman
AU  - Md. Abdul Halim Khan
AU  - Sadikur Rahman Sadik
AU  - Md. Shohanur Rahman Shohan
AU  - Tahzib Mahmud Rifat
AU  - Md. Ashiqussalehin
AU  - Md. Toukir Ahmed
PY  - 2026
DA  - 2026/06/08
TI  - Amigo-Agri: A Human-Following Robotic Platform with Speech Recognition and Retrieval-Augmented Generation for Smart Farming
BT  - Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)
PB  - Atlantis Press
SP  - 842
EP  - 856
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-664-7_58
DO  - 10.2991/978-94-6239-664-7_58
ID  - Hemal2026
ER  -