Environment Monitoring of Rose Crops Greenhouse Based on Autonomous Vehicles with a WSN and Data Analysis

Area of Science:

• Agricultural Engineering
• Robotics
• Environmental Monitoring

Background:

• Greenhouse cultivation of high-value crops like roses requires precise environmental control.
• Traditional monitoring methods can be labor-intensive and may not provide real-time, localized data.
• Optimizing environmental conditions is crucial for crop quality and production timelines.

Purpose of the Study:

• To develop an intelligent monitoring system for greenhouse rose cultivation.
• To improve crop quality and regulate production time through automated environmental control.
• To enable data-driven decision-making for greenhouse management.

Main Methods:

• Development of a system integrating autonomous quadruped vehicles with a wireless sensor network (WSN).
• Implementation of a data analysis process including data balancing, prototype selection, and supervised classification.
• Design of an in-situ intelligent system for real-time decision-making.
• Integration of global positioning system (GPS) for autonomous vehicle route planning.

Main Results:

• The system achieved a significant reduction in the training dataset size obtained from the WSN.
• High classification performance was reached in real-world conditions, with 90% accuracy for one task and 97.5% for another.
• An effective approach for planning and selecting routes for autonomous vehicles was developed.

Conclusions:

• The proposed system enhances environmental monitoring and control in greenhouse rose cultivation.
• Intelligent data analysis and autonomous systems contribute to improved crop quality and production efficiency.
• The integration of WSN, autonomous vehicles, and GPS offers a comprehensive solution for precision agriculture.