Integration of Large Language Models with IoT in Smart Agriculture to Improve Efficiency, Yield, and Quality_Vol. 1 No. 4 (ISE 2024)_Industry Science and Engineering

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Integration of Large Language Models with IoT in Smart Agriculture to Improve Efficiency, Yield, and Quality

DOI: https://doi.org/10.62381/I245403

Author(s)

Tao Feng1, Hao Shen2, Xuecan Yang3, Jean-Marie Nianga4, Zefeng Wang2,3,4,5,*

Affiliation(s)

1Thinker Agricultural Machinery Co.,Ltd, Huzhou, Zhejiang, China 2College of Information Engineering, Anding Honors College, Huzhou University, Huzhou, Zhejiang, China 3IEIP, Institute of Education and Innovation in Paris, Paris, France 4Sino-Congolese Foundation for Development, Brazzaville, Congo 5ASIR, Institute -Association of intelligent systems and robotics, Paris, France *Corresponding Author.

Abstract

This paper examines the novel integration of large language models (LLMs) with Internet of Things (IoT) technologies in the context of smart agriculture. The study emphasizes the transformative impact of LLMs in enhancing data-driven decision-making processes through advanced natural language processing capabilities. By employing IoT devices, comprehensive real-time data on environmental conditions, soil health, and crop status are collected and analyzed using LLMs, thereby facilitating the generation of actionable insights for precision agriculture. Key areas of improvement include optimized irrigation scheduling, targeted pest and disease management, and efficient resource utilization, which collectively contribute to increased crop yields and quality. The findings illustrate the potential of combining LLMs with IoT to create a sustainable, efficient, and high-yield agricultural ecosystem.

Keywords

Large Language Models (LLMs); Internet of Things (IoT); Smart Agriculture; Precision Agriculture; Data-driven Decision Making

References

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