Enhancing IoT based Plant Health Monitoring through Advanced Human Plant Interaction using Large Language Models and Mobile Applications
Kriti Agarwal, Samhruth Ananthanarayanan, Srinitish Srinivasan, Abirami S
TL;DR
The paper presents an AIoT plant health platform that translates real-time soil sensor data into human-language insights about plant health and mood via the Gemini API. It combines a Flutter frontend, Firebase and ThingSpeak-backed real-time data handling, and an LLM-driven chatbot to enable intuitive, conversational plant care. The work documents system architecture, sensor integration, and prompt-based interaction, demonstrating how real-time environmental data can be humanized to enhance personal and agricultural plant management. This approach holds promise for sustaining plant care practices, promoting sustainability, and illustrating practical AI/IoT fusion for natural–human interfaces.
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.