INTELLIGENT SMART AGRICULTURE MONITORING SYSTEM USING IOT AND MACHINE LEARNING

Abstract

This paper presents an  Internet of Things IoT based smart agriculture monitoring system integrated with Machine Learning (ML) to make real time crop monitoring and intelligent descision making. The proposed system employs an ESP32 microcontroller interfaced with multiple sensors, including DHT22 for temperature and humidity, soil moisture , ph, MQ135 air quality, Light Dependent Resistor (LDR), flame, tilt, and rain sensors, to continuously monitor agricultural field conditions.Sensor data is processed using Embedded C programming and transmitted via Wi-Fi to a Python-based application for storage, analysis,  and real-time visualization. A Random Forest Classifier is utilized to analyze environmental parameters and predict crop health by classifying field conditions into Good, Fair, and Poor categories. The system further provides irrigation recommendations, risk assessment, and real-time Telegram notifications to assist farmers in timely decision-making. A relaycontrolled water pump enables automatic irrigation based on soil moisture levels, minimizing water wastage and reducing manual intervention. The proposed system enhances crop productivity, optimizes resource utilization, and promotes sustainable farming through intelligent data-driven monitoring. Its modular architecture also supports future expansion with additional sensors and automation features for precision agriculture.