A Novel Approach for Leveraging Agent-Based Experts on Large Language Models to Enable Data Sharing Among Heterogeneous IoT Devices in Agriculture

The rapid adoption of Internet of Things (IoT) devices in agriculture has led to the generation of diverse data types, creating challenges in data sharing and integration across heterogeneous platforms. This paper presents a novel approach to facilitate data sharing among heterogeneous IoT devices in agriculture using agent-based experts built on large language models (LLMs). Background: Traditional methods of data sharing in agriculture face limitations due to the lack of standardization and interoperability among IoT devices. Previous approaches, such as model fine-tuning and prompt engineering, have shown promise but struggle with open-ended agricultural queries and context comprehension. The proposed Agent-based Data Sharing (ADS) framework combines semantic web technologies with agent-based design and LLMs to enable seamless information exchange, decentralized data sharing, and knowledge transfer through intelligent expert agents. This approach leverages the strengths of LLMs in understanding text and their extensive training data while addressing the challenges of data interoperability and context-aware decision-making in agriculture. Using synthetic agricultural data, we evaluated the framework's performance in disease diagnosis and precision farming recommendations. The results demonstrate significant improvements in data integration, interoperability, and decision-making efficiency. With extensive data sharing, mean performance scores increased by 16% for disease diagnosis and 25% for precision farming compared to baseline scenarios. The framework's ability to manage diverse devices and handle natural language queries through agent-based experts highlights its potential for real-world agricultural applications. This approach could support the advancement of smart farming through IoT applications and pave the way for improved efficiency in sustainable agriculture. However, challenges such as data privacy, standardization, and incentive structures need to be addressed in future research.