Greenhouse agriculture plays a vital role in achieving high crop yield and quality by maintaining a controlled environment. Traditional greenhouse management relies heavily on manual monitoring, which is time-consuming, labour- intensive, and prone to human error. This paper presents an IoT-based Greenhouse Monitoring and Control System using the ESP32 microcontroller. The system integrates multiple sensors — DHT11 for temperature and humidity, a capacitive soil moisture sensor, and an LDR module for light intensity — to continuously track critical environmental parameters. Actuators including a water pump, DC ventilation fan, and LED grow-light are automatically triggered based on predefined thresholds. Sensor data is transmitted over Wi-Fi to the Blynk mobile application for real-time monitoring and remote control from anywhere. Experimental results demonstrate that the system effectively maintains optimal growing conditions, reduces water consumption by approximately 30%, and significantly minimises human intervention, making it a cost-effective and scalable solution for modern greenhouse management.

This paper presented the design and implementation of a cost-effective IoT-based Greenhouse Monitoring and Control System using the ESP32 microcontroller. The system successfully integrates the DHT11 temperature and humidity sensor, a capacitive soil moisture sensor, and an LDR light intensity module to continuously monitor critical environmental parameters, and automatically controls a ventilation fan, irrigation pump, and grow-light based on predefined threshold values. Real-time remote monitoring and control through the Blynk mobile application enables farmers to manage their greenhouse from any location with internet connectivity. Experimental results over a 7-day trial demonstrated that the system reliably maintained optimal growing conditions, reduced water usage by 30%, and minimised manual labour by 70%. The control algorithm executes every 2 seconds, ensuring rapid and responsive actuation. The prototype was developed at a cost of approximately ₹2,500, making it accessible to smallholder farmers. The system is scalable and can be extended to larger greenhouse facilities with additional sensor nodes. Future enhancements include the integration of machine learning algorithms for predictive control, solar power support for energy sustainability, multi-greenhouse management through a centralised cloud dashboard, and the addition of a camera module for plant disease detection using image processing.

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