AI Prediction and Optimization of Environment Monitoring System with IOT for Agriculture Farmers

Abstract

Farmers face challenges such as unpredictable climate conditions, water scarcity, soil degradation, and increasing production costs. Existing monitoring systems provide real-time data but lack predictive intelligence and automated optimization. There is a need for an intelligent system that not only monitors environmental parameters but also predicts future conditions and optimizes resource utilization to maximize crop yield and minimize operational costs. This paper presents an AI-based prediction and optimization framework for an IoT-enabled environmental monitoring system designed to support smart agriculture. The system integrates multiple field sensors to continuously monitor key environmental parameters such as soil moisture, temperature, humidity, pH, light intensity, rainfall, and CO₂ levels. Data collected through the Internet of Things (IoT) architecture is transmitted to a cloud or edge computing platform, where Artificial Intelligence (AI) and machine learning models analyze historical and real-time data to predict climatic variations, soil conditions, crop health, and yield outcomes. Advanced optimization algorithms are employed to automate irrigation scheduling, fertilizer application, and energy management, thereby reducing water consumption, operational costs, and human intervention. The proposed system enhances decision-making accuracy, improves crop productivity, and promotes sustainable and resource-efficient farming practices, making it highly suitable for precision agriculture and climate-resilient farming environments.