IOT-BASED SMART FARM ENVIRONMENTAL CONDITION CLASSIFICATION FOR GREENHOUSE AUTOMATION

Abstract

The Internet of Things (IoT) plays an important role in modern agriculture by improving
productivity and reducing manual effort. This project, IoT Based Smart Farm Environmental
Condition Classification for Greenhouse Automation, focuses on monitoring and controlling
greenhouse environmental conditions using smart sensors and machine learning techniques.
Various sensors such as temperature, humidity, soil moisture, and light sensors are used to collect
real-time data from the greenhouse environment. The collected data is processed and analyzed
using a machine learning classification model to determine the environmental condition and
decide whether the water pump actuator should be turned ON or OFF. The system helps farmers
maintain optimal conditions for plant growth by automatically controlling irrigation and
environmental parameters. A web-based interface is also developed to visualize sensor data and
prediction results. This smart greenhouse automation system improves efficiency, saves water,
reduces human intervention, and ensures better crop management. The implementation of IoT
and machine learning technologies provides a reliable and cost-effective solution for modern
smart farming.
The IoT-Based Smart Farm Environmental Condition Classification system is designed to
enhance greenhouse automation through real-time monitoring and intelligent decision-making.
Traditional farming methods often rely on manual observation, which can lead to inefficient
resource utilization and delayed responses to environmental changes. This system integrates IoT
sensors to continuously collect data such as temperature, humidity, soil moisture, and light
intensity from the greenhouse environment.