Title: Integrating language models with sensor data for enhanced plant health monitoring and query response

Authors: Param Ahir; Nikunj Tahilramani

Addresses: School of Cyber Security and Digital Forensics, National Forensic Sciences University, Gandhinagar, Gujarat, India ' ClearConsent-Data Sentinel Technologies, Ahmedabad, Gujarat, India

Abstract: The integration of environmental sensors with large language models, such as ChatGPT, led to an advancement in the domain of plant health monitoring. This study investigated the correlation between the simulation of environmental sensor data, such as soil moisture, temperature and humidity and the use of language models to improve plant care. By simulating sensor data, we aim to closely approximate the complexity of the natural environment, acknowledging the inherent challenges and limitations. This provides better adaptability between various plant species and different environmental circumstances. By integrating language models, people can participate in more authentic conversations and receive customised recommendations. This study examines the potential of language models and the Internet of Things to enhance the effectiveness of plant health monitoring systems. The results show that additional knowledge acquired through such a large language model can help in utilising sensor data in an efficient manner.

Keywords: large language models; internet of things; plant health monitoring; artificial intelligence.

DOI: 10.1504/IJCAT.2024.146146

International Journal of Computer Applications in Technology, 2024 Vol.75 No.2/3/4, pp.178 - 187

Received: 12 Mar 2024
Accepted: 18 Dec 2024
Published online: 07 May 2025 *

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