# Real-Time Remote Monitoring of Environmental Conditions and Actuator Status in Smart Greenhouses Using a Smartphone Application

## TL;DR

A smartphone app was developed to monitor and control smart greenhouses in real-time using wireless sensors and actuators.

## Contribution

A novel Android-based system integrating multi-layer wireless sensing, LoRaWAN, MQTT, and a smartphone interface for real-time greenhouse monitoring.

## Key findings

- The system detected vertical and spatial environmental variability in greenhouses using real-time data and 3D maps.
- Abnormal conditions like threshold violations and sensor inconsistencies were identified and notified in real-time.
- Energy consumption profiling showed peak power usage of 3.5 W and CPU utilization between 40-70% during active monitoring.

## Abstract

Advancement of precision agriculture increasingly relies on cost-effective and scalable technologies for real-time environmental management, particularly in greenhouse environments where vertical and spatial microclimate heterogeneity influences crop performance. This study presents the design, implementation, and experimental validation of an Android-based smartphone application edge supervisory monitoring system integrated with multi-layer wireless sensing and control nodes for real-time monitoring in a smart greenhouse. The system combined multi-layer wireless sensor nodes, wireless control nodes, a Long-Range Wide Area Network (LoRaWAN) gateway, Message Queuing Telemetry Transport (MQTT) communication, and a cloud-synchronized smartphone-based supervisory interface for visualizing environmental data, detecting defined abnormal events, and controlling actuators remotely. For feasibility tests, 54 sensing nodes and 12 actuator nodes were deployed across three vertical layers in two sections, measuring temperature, humidity, CO2 concentration, and light intensity. Abnormality was defined as environmental threshold violations, statistical signal deviations, actuator power inconsistencies, and communication timeout events. Experimental results revealed vertical and spatial environmental variability across greenhouse sections, while real-time time-series and 3D spatial maps enabled the rapid detection of abnormal conditions. The rule-based abnormality detection engine identified out-of-range environmental values and sensor-related inconsistencies and generated immediate notifications. Smartphone profiling revealed that display and system-level processes accounted for energy consumption, with battery power reaching a peak of 3.5 W and application CPU utilization ranging from 40% to 70% during active monitoring. The results demonstrate system-level feasibility, responsiveness, and scalability under commercial greenhouse workloads, supporting future integration of predictive control and energy-efficient operation.