Published Paper Details:

Greenhouses provide a controlled environment that enhances plant growth by regulating temperature, humidity, and light. However, managing these environmental factors manually can be challenging and may lead to reduced crop productivity. The project presents a Greenhouse Monitoring and Control System that uses various sensors and a control unit to automate the regulation of greenhouse conditions. The system continuously monitors critical factors such as temperature, soil moisture, and light intensity, adjusting the environment by turning on fans, activating watering pumps, and controlling artificial lighting as needed. The system incorporates machine learning algorithms to analyze sensor data and predict which crops are best suited for the current conditions. By offering crop recommendations based on real-time environmental factors, the system enables farmers to make informed decisions, optimizing crop selection for maximum yield and profitability. This project aims to reduce human error, improve farming efficiency, and increase sustainability by automating key greenhouse functions and providing data-driven insights to enhance productivity.