Iot-based smart greenhouse monitoring and automation system

Overview

This solution is an IoT-based smart environment system designed for greenhouses and controlled agricultural environments. It continuously monitors critical growing parameters including soil moisture, air temperature, humidity, air quality, and light exposure, then automatically triggers corrective actions such as irrigation, ventilation, and supplemental lighting. Sensor data is uploaded to the cloud for remote visualization and analysis, enabling growers to manage conditions from anywhere and respond quickly to changes that affect crop health and yield.

Technical specifications

Core components:

• NodeMCU with Wi-Fi module serves as the central controller, interfacing sensors with cloud IoT platforms
• Raspberry Pi board coordinates connected components and manages sensor-to-actuator communication
• Soil moisture sensor and motor pump assess and maintain optimal moisture levels for crop growth
• DHT11 and LM35 sensors monitor temperature and humidity to maintain ideal growing conditions
• MQ2 and MQ135 gas sensors combined with an exhaust fan monitor and manage air quality
• Artificial LED lighting and LDR sensor regulate light exposure for photosynthesis requirements
• Relay modules switch actuators on and off based on sensor thresholds

Software and analytics:

• Cloud-based data collection and storage for historical analysis
• Dashboards built with Blynk and ThingSpeak for real-time visualization and remote control
• Automated gap-filling algorithm that estimates missing data using previous readings when connectivity is interrupted
• Configurable thresholds, such as a maximum temperature of 28°C and soil moisture between 50% and 80%

Technology readiness level

The system has been implemented and validated in a greenhouse testbed, with sensor outputs confirmed under expected operating conditions. Validation has covered the basic circuit integration, sensor-to-actuator response, and cloud dashboard connectivity. Future work focuses on expanding the platform with additional sensors and refining the automated control logic for broader crop types and larger-scale deployments. The solution is positioned for pilot trials and field validation with agricultural partners.