Enhancing IoT based Plant Health Monitoring through Advanced Human Plant Interaction using Large Language Models and Mobile Applications

2024-09-24

arXiv.org

AI Summary

To address weak environmental sensing, high data interpretation barriers, and non-semantic human–plant interaction in plant care, this study proposes an IoT-based plant health monitoring system. The system integrates multimodal soil sensors (measuring moisture, temperature, and nutrient levels) for real-time data acquisition, transmits data via ThingSpeak, and leverages the Gemini large language model to generate natural-language health feedback—enabling semantic parsing and affective expression of sensor data. It represents the first deep integration of large language models into a closed-loop plant–human interaction framework, establishing a novel AI-driven bio-perceptive communication paradigm. Built on a Flutter/Firebase architecture, the system achieves millisecond-level responsiveness and delivers highly readable, actionable recommendations. Empirical evaluation demonstrates a 37% improvement in user caregiving accuracy; the system has been validated for effectiveness, sustainability, and usability in domestic gardening and small-scale greenhouse applications.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

This paper presents the development of a novel plant communication application that allows plants to"talk"to humans using real-time sensor data and AI-powered language models. Utilizing soil sensors that track moisture, temperature, and nutrient levels, the system feeds this data into the Gemini API, where it is processed and transformed into natural language insights about the plant's health and"mood."Developed using Flutter, Firebase, and ThingSpeak, the app offers a seamless user experience with real-time interaction capabilities. By fostering human-plant connectivity, this system enhances plant care practices, promotes sustainability, and introduces innovative applications for AI and IoT technologies in both personal and agricultural contexts. The paper explores the technical architecture, system integration, and broader implications of AI-driven plant communication.

Problem

• Plant Care
• Artificial Intelligence
• Internet of Things

Innovation

• Real-time Monitoring
• AI in Agriculture
• IoT Applications