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Real-Time Remote Monitoring of Environmental Conditions and Actuator Status in Smart Greenhouses Using a Smartphone
Emmanuel Bicamumakuba1, Md Nasim Reza1,2, Hongbin Jin2
1Department of Agricultural Machinery Engineering, Graduate School, Chungnam National University, Daejeon 34134, Republic of Korea.
Sensors (Basel, Switzerland)
|March 14, 2026
Summary
A new Android app system with wireless sensors provides real-time monitoring for smart greenhouses. This precision agriculture technology detects environmental anomalies, improving crop management and operational efficiency.
Area of Science:
- Precision Agriculture
- Smart Greenhouse Technology
- Agricultural IoT
Background:
- Precision agriculture demands cost-effective, scalable technologies for real-time environmental management.
- Greenhouse microclimate heterogeneity significantly impacts crop performance.
- Existing systems often lack integrated, multi-layer monitoring and control capabilities.
Purpose of the Study:
- To design, implement, and validate an Android-based smartphone application edge supervisory monitoring system.
- To integrate multi-layer wireless sensing and control nodes for real-time greenhouse monitoring.
- To enable remote visualization, abnormality detection, and actuator control in smart greenhouses.
Main Methods:
- Developed an Android application integrated with wireless sensor/actuator nodes and a Long-Range Wide Area Network (LoRaWAN) gateway.
- Utilized Message Queuing Telemetry Transport (MQTT) for communication and cloud synchronization.
- Deployed 54 sensing and 12 actuator nodes across three vertical layers to measure temperature, humidity, CO2, and light intensity.
Main Results:
- Experimental validation confirmed vertical and spatial environmental variability within greenhouse sections.
- The system successfully detected abnormal conditions, including environmental threshold violations and sensor inconsistencies, through real-time monitoring and 3D spatial maps.
- Smartphone profiling indicated energy consumption patterns, with peak power draw and application CPU utilization quantified during active monitoring.
Conclusions:
- The developed system demonstrates feasibility, responsiveness, and scalability for commercial greenhouse workloads.
- Real-time monitoring and rule-based abnormality detection enhance environmental management and enable rapid response to issues.
- The system supports future advancements in predictive control and energy-efficient operation for smart agriculture.
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