Arduino-Based Sensor System Prototype for Microclimate Monitoring of an Experimental Greenhouse ^†

Abstract

Arduino-based sensor systems are gaining widespread adoption in modern technological applications due to their accessibility, low-cost components, diverse sensor compatibility, high reliability, and user-friendly programming. Because of these advantages, such a system was selected to monitor and control microclimate parameters in a small-scale experimental greenhouse. The greenhouse will cultivate several vegetable species in soils with varying zeolite concentrations. The aim of this paper is to present the design and prototype development of a sensor system capable of tracking key environmental parameters, including temperature, humidity, atmospheric pressure, and soil moisture, while also enabling automated irrigation.

1. Introduction

Embedded systems have become ubiquitous in modern technological applications, driven by their ability to perform dedicated tasks with high reliability and real-time responsiveness [1], particularly in industrial automation and IoT domains where deterministic operation is critical.

Unlike general-purpose computing architectures, embedded systems employ purpose-built designs that eliminate resource-intensive peripherals [2], instead utilizing optimized interfaces (e.g., MEMS sensor arrays, capacitive touch inputs) to achieve ultra-low-power operation (<1 W typical) and compact form factors, as shown in

Table 1. Comparative analysis of architectural features between general-purpose computing systems and embedded systems.

Future	General-Purpose	Embedded Systems
Power Consumption	50–100 W	0.1–1 W
Interface Types	Standard I/O	Application-Specific
Typical Use	Data Processing	Real-Time Control

.

This architectural efficiency has enabled transformative adoption across vertical markets, particularly in precision agriculture, where distributed sensor networks require both energy autonomy and environmental robustness [3].

Within this landscape, the Arduino ecosystem has established itself as a paradigm-shifting platform for rapid prototyping and cost-optimized automation [4]. Its open-source architecture integrates three key innovation vectors: (1) a versatile microcontroller unit (MCU) with mixed-signal I/O capabilities (10-bit ADC, PWM outputs), (2) configurable digital/analog interfaces supporting over 20 sensor protocols (I²C, SPI, 1-Wire), and (3) modular expansion through standardized hardware shields (e.g., GSM, LoRa, PLC) and object-oriented software libraries [5]. This tripartite flexibility has enabled cross-domain solutions spanning from precision environmental monitoring to closed-loop robotic systems, with documented deployment in over 85% of academic prototyping projects [6].

However, Arduino’s accessibility comes with inherent constraints:

• Electrical limitations: TTL-level signals (0–5 V) and limited current sourcing (≤20 mA per pin) necessitate external circuitry (e.g., amplifiers, optoisolators) for industrial integration [7].
• Computational bounds: Finite memory and clock speeds restrict complex algorithms, often requiring optimization or hybrid architectures [8].

Despite these challenges, Arduino remains a preferred choice for experimental systems, as evidenced by its use in microclimate monitoring (e.g., [9,10]). This paper leverages Arduino’s strengths to address agritech needs, focusing on a low-cost sensor network for greenhouse automation—a critical step toward sustainable precision agriculture [11].

The primary application of this sensor system is going to be for monitoring and controlling microclimate parameters within a smart greenhouse, where multiple vegetable species will be cultivated in soils with varying zeolite concentrations.

Our sensor system employs real-time data logging to calibrate environmental parameters, ensuring operational stability.

2. Design and Development

The designed prototype integrates an Arduino Mega platform (Atmega2560 microcontroller) with the following components:

• Sensors:
  Resistive soil moisture sensors;

  -

  Temperature and humidity sensors (DHT22);

  -

  Barometric pressure sensor (BMP180).

• Data logging: SD card module storing measurements in Excel-compatible format
• Peripherals:

  -

  GPS module for geolocation tracking.

  -

  Real-time clock (RTC) for timestamping and background task scheduling.

  -

  Four-line LCD display for the control panel.

• Software Architecture:
  The embedded operating system was programmed in C++ (Arduino IDE) and features a control panel with the following menu options:

  -

  Sensor Status (real-time readings);

  -

  Temperature/Humidity Monitoring;

  -

  Time/Date Configuration (RTC sync);

  -

  Barometric Pressure Status;

  -

  GPS Coordinates;

  -

  File System Management (SD card data);

  -

  Environmental Actuators Schedule (fan/light triggering based on time thresholds);

  -

  System Info (firmware version, memory usage).

  Detailed block diagram of the sensor system is presented in Figure 1.

The Arduino Mega platform was selected as the core controller due to its

(1) Extensive GPIO pin count (54 digital I/O pins);

(2) High-density analog-to-digital conversion capability (16-channel 10-bit ADC);

(3) Expanded memory resources (256 KB flash, 8 KB SRAM)-critical features for simultaneous multi-sensor integration and data processing in our greenhouse monitoring system.

A general overview of the platform architecture is presented in Figure 2.

The schematic diagram (Figure 3) presents the full electrical implementation of the monitoring system.

To achieve full system functionality, the following peripheral components were selected and integrated

• A 20 × 4 character LCD (Liquid Crystal Display) with I²C interface (Figure 4).

Real-time clock (RTC): The DS1302 model was chosen, which operates in Low Power Consumption mode and stores the time and date in Unix format. It communicates with the microcontroller via a three-channel serial interface (Data, Clock, Reset) (Figure 5).

Figure 4. A 20×4 character LCD.

Figure 4. A 20×4 character LCD.

Figure 5. RTC-DS1302.

Figure 5. RTC-DS1302.

Resistive soil moisture sensor with operational amplifier conditioning (Figure 6).

Table 2. Technical specifications—resistive soil moisture sensor.

Parameter	Specification
Sensor Type	Resistive (2-probe)
Output Range	0–3.0 V (after amplification)
Measurement Range	0–100% VWC (volumetric water content)
Accuracy	±2% in 10–60% VWC range
Amplifier Circuit	LM358 op-amp (non-inverting, gain = 15)
Calibration	Factory-calibrated for mineral soils

shows its main characteristics.

The system employs a DHT22 digital sensor for atmospheric measurements (Figure 7), with the following metrological characteristics (

Table 3. Metrological characteristics of sensor DHT22.

Parameter	Specification
Temperature Range	−40 °C to +80 °C
Temperature Accuracy	±0.5 °C (typ. ±0.3 °C @25 °C)
Humidity Range	0−100% RH
Humidity Accuracy	±2% RH (20−80% RH), ±5% RH (extended)
Resolution	0.1 °C/0.1% RH
Sampling Rate	0.5 Hz (2 s interval)
Interface	Single-wire digital (Custom protocol)
Operating Current	1.5 mA (during measurement)

).

U-blox NEO-6M GPS module (Figure 8) with the following technical implementation (

Table 4. Metrological characteristics of GPS module.

Parameter	Specification
Receiver Type	50-channel u-blox 6 positioning engine
Positioning Accuracy	2.5 m CEP (SBAS enabled)
Time-To-First-Fix	34 s (cold), 1 s (hot)
Communication Interface	UART (default 9600 baud)
Antenna Type	Passive patch antenna (25 × 25 mm)
Update Rate	1 Hz (configurable up to 5 Hz)
Operating Voltage	3.3 V (with onboard 3.3 V LDO)

).

For atmospheric pressure monitoring the system incorporates a Bosch BMP280 digital barometric pressure sensor (Figure 9) with the technical specifications shown in

Table 5. Metrological characteristics of module BMP280.

Parameter	Specification
Pressure Range	300–1100 hPa (±1 hPa absolute accuracy)
Temperature Range	−40 °C to +85 °C
Interface	I2C (default address 0 × 76/0 × 77)
Resolution	0.01 hPa (16-bit ADC)
Sampling Rate	Up to 182 Hz (ultra-high-res mode)
Power Consumption	2.7 μA @1 Hz sampling

).

• The system implements an SPI-based SD card module for Excel-compatible data storage (Figure 10).

3. Results and Discussion

After the electrical circuit diagram of the sensor system for monitoring greenhouse parameters was synthesized and the individual modules and components were delivered, the system prototype was fully implemented. The system’s software was developed in C++. The capabilities provided by the sensor system are as follows:

When power is turned on, the user settings are loaded from the microcontroller’s EEPROM memory, and the main menu is displayed on the screen.

From the main menu, the user can select the following submenus: Start; Temperature; GPS; Real-Time Clock (RTC); Barometer; Data Storage; Greenhouse Environment Control; and System Information. The Temperature, GPS, and Barometer menus are read-only and contain no configurable settings. The Soil menu allows the user to check the moisture levels in each section, set the moisture percentage threshold for activating irrigation, and set the stop threshold for the irrigation pumps. The microcontroller uses this information to control the relay switches for the water pumps. The RTC menu enables the user to configure the system date and time, which are critical for the microcontroller’s timer functionality. The Data Storage menu is used to set the logging interval for sensor data storage. All sensor readings are saved on an SD card in tabular format. The Environment Control menu allows configuration of fan operation cycles (ON/OFF intervals) and configuration of artificial lighting (start/stop times). The System Information menu provides options for software updates and system reboot.

The system is powered by a 12 V battery with a corresponding battery charger. Future upgrades include the integration of solar panels and a solar charge controller. This enhancement will make the system mobile and fully autonomous.

4. Conclusions

In this study, we discussed the design of a sensor system which is capable of tracking key environmental parameters such as temperature, humidity, atmospheric pressure, and soil moisture. The irrigation process is MCU-controlled and thus fully automatic. The sensor system features input of user settings in order to perform the whole process of monitoring and sensing according to a user-defined scenario. Moreover, a physical prototype of the sensor system has been built and implemented.

The primary advantage of the proposed sensor system prototype lies in its ability to enable automated, real-time monitoring and precise control of environmental parameters within experimental greenhouse conditions.

As future upgrade of the system is regarded the integration of solar panels and solar charge controller. This enhancement will make the system mobile and fully autonomous.