AUTOMATED SYSTEM FOR GREEN HOUSE AGRICULTURE WITH ML FERTILITY ADVISIOR

PRASANNA K.M; ABDUL MATHEEN N; SWATHI R; REVATHI E; REKHA R

AUTOMATED SYSTEM FOR GREEN HOUSE AGRICULTURE WITH ML FERTILITY ADVISIOR

Authors: PRASANNA K.M, ABDUL MATHEEN N, SWATHI R, REVATHI E, REKHA R

Unique Paper ID: 178010
Volume: 11
Issue: 12
PageNo: 2969-2973

Keywords: No Keywords Found

Abstract:
In modern agriculture, precise monitoring and control of greenhouse environmental parameters are essential for optimal plant growth and productivity. Traditionally, this requires manual checks and labor-intensive processes, which are prone to inefficiency and human error. To address these challenges, we propose an IoT-based greenhouse monitoring and automation system that incorporates Machine Learning to enhance crop yield. The system’s hardware includes the DHT11 sensor for measuring temperature and humidity, the MQ2 gas sensor for detecting harmful gases, and a soil moisture sensor to monitor water requirements. Data from these sensors is transmitted to an ESP32 microcontroller, which processes the information to automate irrigation. Based on the soil moisture levels, an irrigation motor is activated, ensuring precise water distribution. All sensor data is sent to Google Sheets for remote access, allowing real-time monitoring of greenhouse conditions. On the software side, a Windows application, PlantPulse, was developed for remote monitoring and control. The application provides a user-friendly interface for viewing sensor data and includes a machine learning model that recommends suitable fertilizers. The model analyzes soil type, temperature, humidity, and plant growth milestones to offer tailored fertilizer suggestions. This minimizes human intervention while maintaining optimal growing conditions, reducing operational costs, and improving agricultural efficiency. By combining IoT, automation, and machine learning, this project provides a comprehensive solution for modern agriculture, optimizing resource use, enhancing sustainability, and improving the productivity of greenhouse operations.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 12
PageNo: 2969-2973

AUTOMATED SYSTEM FOR GREEN HOUSE AGRICULTURE WITH ML FERTILITY ADVISIOR

Available:https://ijirt.org/Article?manuscript=178010

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UGC Approved
Journal no 47859

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