A SURVEY ON AI – BASED PLANT HEALTH MONITORING SYSTEM

Abstract: The increasing demand for sustainable agriculture and early stress management in crops has led to the exploration of advanced technologies such as Artificial Intelligence (AI) for plant monitoring and communication. Plants continuously emit biochemical and biophysical signals in response to environmental stresses including drought, nutrient deficiency, pest attacks, and diseases. Recent advancements in AI, sensor technologies, and Internet of Things (IoT) systems enable the detection, interpretation, and translation of these signals into actionable insights for farmers.This study focuses on the development of an AI-based plant communication and stress detection system that integrates Machine Learning algorithms, sensor networks, and data analytics to monitor plant health in real time. The system captures parameters such as Volatile Organic Compounds (VOCs), leaf temperature, soil moisture, and electrical signaling patterns, which are analyzed using AI models to identify stress conditions at an early stage. By enabling plants to communicate their stress signals, this approach facilitates timely intervention, reduces crop losses, and minimizing excessive use of water, fertilizers, and pesticides.The proposed framework supports precision agriculture by improving decision-making and promoting resource-efficient farming practices. Furthermore, it contributes to sustainable crop management by enhancing resilience against climate variability and environmental challenges. The integration of AI in plant health monitoring represents a transformative step toward smart agriculture and improved global food security.

Keywords: Artificial Intelligence, Plant communication, Stress detection, Precision agriculture, IoT, Plant health.