Integration of IoT, Machine Learning, and Sensors for Intelligent Environmental Monitoring and Agricultural Development

This paper presents an integrated framework for intelligent agricultural monitoring and development by combining Internet of Things (IoT) technology, machine learning algorithms, sensor networks and custom hardware design. A comprehensive system was developed using environmental sensors including DHT11, soil moisture probes, BMP180 pressure modules, MQ‐4 gas detectors, rain detection sensors and HC‐SR04 ultrasonic modules, interfaced via custom‐designed printed circuit boards (PCBs) fabricated using Proteus software. NodeMCU ESP8266, ESP32 DevKit and ESP32‐CAM microcontrollers served as the hardware backbone for real‐time data acquisition, wireless transmission, and image capture. Collected sensor data were transmitted to cloud platforms through Adafruit IO for remote visualization and analysis. Machine learning models, including Random Forest and XGBoost classifiers, were trained on features extracted from VGG16‐based image processing to classify plant health conditions with high accuracy. Intelligent irrigation control was achieved through autonomous decision‐making based on real‐time sensor feedback and environmental conditions, dynamically activating a water pump system. The integration of low‐power hardware, efficient PCB layouts, cloud‐based dashboards, and lightweight machine learning models resulted in a scalable, portable, and cost‐effective smart farming solution. Experimental results validate the system’s capability for accurate environmental monitoring, efficient resource utilization, and intelligent crop management, offering significant potential for sustainable agriculture in resource‐constrained settings.