Abstract
The Smart Greenhouse Monitoring and Control System using IoT and Predictive Analytics aims to revolutionize modern agriculture by integrating intelligent automation with data-driven decision-making. The system utilizes IoT sensors to continuously monitor key environmental parameters such as temperature, humidity, soil moisture, and light intensity inside the greenhouse. The collected data is visualized in real-time on an interactive dashboard using graphical representations and stored for historical trend analysis of different crops.
Through predictive analytics, the system analyzes historical data to recommend optimal crop selection and fertilizer usage according to seasonal patterns and climatic conditions. A manual crop selection feature allows the user to set the crop for the current season. When the measured parameters deviate from the set thresholds, the system automatically activates the respective actuators—such as water pumps, fans, or light sources—to maintain ideal growing conditions.
This intelligent approach enhances resource efficiency, minimizes human intervention, and increases crop yield by ensuring that plants receive precise amounts of water, nutrients, and environmental care. Overall, the project demonstrates how IoT combined with predictive analytics can support sustainable, automated, and data-driven agriculture for future smart farming practices.
Introduction
Agriculture plays a vital role in sustaining human life, but traditional farming methods often face challenges such as unpredictable weather, inefficient resource use, and lack of real-time monitoring. To overcome these issues, the integration of Internet of Things (IoT) and predictive analytics in agriculture offers a modern and efficient solution.
The Smart Greenhouse Monitoring and Control System is designed to automate and optimize crop growth by continuously monitoring environmental conditions such as temperature, humidity, soil moisture, and light intensity. Using IoT sensors, data is collected and transmitted to a centralized platform, where it is stored and visualized through an interactive dashboard. This dashboard not only displays real-time sensor readings but also maintains historical data for different crops, helping farmers analyze seasonal variations and crop performance over time.
Predictive analytics is applied to this data to recommend suitable crops and fertilizer plans based on climatic conditions and seasonal changes. The system also allows manual crop selection, where preset threshold values are automatically loaded for irrigation, lighting, and temperature control. Whenever these environmental conditions deviate from the ideal range, the system automatically activates the corresponding devices—such as water pumps, fans, or artificial lights—to restore optimal conditions.
By combining automation, data analysis, and intelligent control, this project aims to improve crop yield, reduce wastage of water and fertilizers, and promote sustainable smart farming practices. It demonstrates how technology can transform agriculture into a data-driven and self-regulating system that ensures consistent productivity regardless of external environmental factors.

Objectives
1. To monitor temperature, humidity, soil moisture, and light using IoT sensors.
2. To collect and store sensor data for future use and analysis.
3. To display real-time and historical data on a user-friendly dashboard.
4. To use predictive analytics for crop and fertilizer recommendations.
5. To allow manual crop selection and automatically set threshold values.
6. To control water, temperature, and light automatically based on sensor readings.
7. To save water, fertilizer, and energy through smart automation.
8. To improve crop growth and support sustainable farming practices.