Integration of LabVIEW-Compatible IoT Boards for Smart Agricultural Monitoring: A Comprehensive Framework Using Raspberry Pi, Arduino, and ESP32

Abstract

This paper presents a comprehensive smart agricultural monitoring system integrating LabVIEW with three prominent IoT platforms Raspberry Pi, Arduino, and ESP32 for real-time crop monitoring and automated precision irrigation. We propose a novel three-tier architecture comprising distributed sensor nodes. a Raspberry Pi edge gateway with local processing capabilities, and LabVIEW-based visualization and control. The system implements MQTT-based communication for seal-able wireless deployment and serial protocols for high-reliability wired connections. Experimental evaluation over a 90-day field deployment on a 2-hectare vegetable farm demonstrates sensor accuracy within 2.3% of reference instruments, 31.4% water consumption reduction through precision irrigation, and 18.7% average crop yield improvement across tomato, pepper, and cucumber crops. Platform-specific analysis reveals that ESP32 offers optimal cost-performance for wireless sensing at $1.50 per node with deep sleep power consumption of 10 A. Arduino pro videos superior LabVIEW integration via the LINX toolkit with 99.7% uptime, and Raspberry Pi excels as an edge gateway supporting local ma chine learning inference. The system achieved complete payback within 15 days, demonstrating compelling economic viability for smallholder and commercial agricultural operations.

Introduction

The text discusses the development and application of IoT-based precision agriculture systems integrated with LabVIEW and multiple hardware platforms to improve irrigation efficiency, resource management, and crop productivity.

1. Background and Problem

Agriculture consumes about 70% of global freshwater, yet traditional irrigation systems waste up to 60% of water due to:

• Evaporation
• Runoff
• Poor scheduling

With the global population expected to reach 9.7 billion by 2050, food production must increase significantly while reducing environmental impact. This creates a need for efficient, data-driven farming solutions.

2. Role of IoT in Smart Agriculture

IoT-based precision agriculture uses sensors and connected devices to:

• Monitor soil moisture, temperature, humidity, and light
• Enable real-time decision-making
• Optimize irrigation and resource use

Wireless Sensor Networks (WSNs) and low-cost microcontrollers have made smart farming more accessible across different farm sizes.

3. Use of LabVIEW in Agriculture

LabVIEW is highlighted as a key tool for:

• Data acquisition
• Instrument control
• Industrial and agricultural automation

Its visual programming interface makes it easier for non-programmers to develop monitoring systems.

When combined with IoT hardware, LabVIEW enables efficient development of smart irrigation and monitoring solutions.

4. Research Gap

Despite many IoT agriculture studies, there is limited work on:

• Integrating multiple hardware platforms with LabVIEW
• Providing unified system frameworks
• Validating real-world deployments with long-term testing

5. Proposed Contributions

The paper addresses these gaps by proposing:

• A three-tier architecture integrating:
  • Raspberry Pi 4
  • Arduino
  • ESP32
    with LabVIEW
• A comparative study of platforms based on:
  • Power usage
  • Processing capability
  • Cost
  • Connectivity
• A 90-day field deployment showing improvements in:
  • Sensor accuracy
  • System reliability
  • Water savings
  • Crop yield
• An economic analysis proving fast return on investment

6. Related Work Summary

IoT in Agriculture

Research shows:

• Rapid growth in smart agriculture systems
• Water savings of 20%–50% using smart irrigation
• Machine learning improves irrigation scheduling

Wireless Sensor Networks (WSN)

• Enable real-time monitoring in agriculture
• Challenges include power limits and environmental interference
• Edge computing improves reliability and reduces latency

Platform-Specific Findings

• ESP32: Low power, wireless-ready, ideal for large-scale sensing
• Raspberry Pi: High processing power for analytics and AI tasks
• Arduino: Reliable real-time control for sensors and actuators

LabVIEW Integration

• Used successfully in greenhouse automation and monitoring
• Integration methods include LINX toolkit, MQTT, TCP/IP
• Multi-platform integration remains underexplored

7. Platform Comparison

• Arduino: Best for real-time sensor control but lacks connectivity
• Raspberry Pi: Powerful edge computing but high power consumption
• ESP32: Low-cost, energy-efficient, ideal for distributed IoT systems

8. Proposed System Concept

The system uses a layered architecture combining all three platforms to:

• Collect sensor data efficiently
• Process data at edge and central nodes
• Enable smart irrigation decisions through LabVIEW integration

Conclusion

This paper presented a comprehensive framework for integrating Arduino, Rasp berry Pi. and ESP32 platforms with LabVIEW for smart agricultural monitoring, validated through a 90-day field deployment on a 2-hectare vegetable farm. The experimental results demonstrated sensor accuracy within 1.8-4.1% mean error across five environmental parameters (R2 > 0.94), system reliability of 98.4-99.9% uptime with Arduino achieving the highest reliability at 99.7% for wired installations, and substantial agricultural benefits including 31.4% wa-ter consumption reduction through real-time monitoring and weather-responsive scheduling, along with 18.7% average crop yield improvement across tomato, pepper, and encumber crops. The economic analysis revealed compelling viability with a 45-day payback period and 709% first-year ROI on a total hardware investment of $455. Platform-specific findings indicate that ESP32 offers optimal cost-performance for wireless distributed sensing at $4.50 per node with ultra-low power deep sleep capability. Arduino provides superior LabVIEW integration through the LINX toolkit and maximum reliability for actuator control applications, while Raspberry Pi excels as an edge gateway supporting local ma chine learning inference and protocol translation. The successful integration of LabVIEW with these low-cost loT platforms demonstrates that precision agriculture technologies can be democratized, making data-driven farming accessible and economically viable for agricultural operations of all scales.

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Copyright

Copyright © 2026 Niket Amoda, Het Parekh, Shweta Sharma, Ankit Patel. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.