زاده et al. (2026) Energy-autonomous IoT-based wireless sensor networking architecture for plant health monitoring and precision irrigation in sugarcane

Identification

• Journal: Smart Agricultural Technology
• Year: 2026
• Date: 2026-01-15
• Authors: سامان آبدانان مهدی زاده, Jamal Mohammadi Moalezadeh, Amirabbas Abin
• DOI: 10.1016/j.atech.2026.101800

Research Groups

• Department of Mechanics of Biosystems Engineering, Faculty of Agricultural and Rural Development, Agricultural Sciences and Natural Resources University of Khuzestan, Ahvaz, Khuzestan, Iran
• Remote Sensing and GIS, Sugarcane & By-products Development Research & Training Institute of Khuzestan, Khuzestan, Iran
• Agricultural Audit and Monitoring, Sugarcane & By-products Development Research & Training Institute of Khuzestan, Khuzestan, Iran

Short Summary

This paper introduces a self-sustaining IoT framework with a wireless sensor network for real-time sugarcane health monitoring and precision irrigation. The system achieved high data reliability, early stress detection, and significant water savings through energy-autonomous operation.

Objective

• Develop a unified system for environmental monitoring, plant health assessment, and precision irrigation.
• Implement hierarchical power management for extended off-grid operation.
• Integrate Crop Water Stress Index (CWSI)-based stress detection with multi-parameter decision making.
• Provide accessible interfaces for farm managers.

Study Configuration

• Spatial Scale: A 12,000-hectare commercial sugarcane field in Khuzestan, Iran (Latitude: 31.8980, Longitude: 48.3670).
• Temporal Scale: Six-month field deployment (March to October).

Methodology and Data

• Models used:
  • Crop Water Stress Index (CWSI) empirical formula (Eqs. 1-6)
  • Tetens equation (for saturation vapor pressure computation)
  • Regression model (for converting analog-to-digital converter output to water level)
• Data sources:
  • Sensors: HTU21D (ambient temperature and relative humidity), MLX90615 (plant temperature), Logitech C920 HD webcam (plant height images), soil-embedded dielectric permittivity sensor (soil moisture at 0.20 m depth), vertically suspended dielectric displacement probe (groundwater table fluctuations up to 2 m depth).
  • Validation Data: Gravimetric measurements for soil moisture, manual ruler measurements for plant height.
  • System Components: Raspberry Pi 2 (central processing unit), ATmega8 microcontroller (low-power timekeeping), 20,000 mAh (74 Wh) lithium-ion power bank, 10 watt monocrystalline solar panel.

Main Results

• Soil moisture measurement accuracy showed a strong correlation (R² = 0.96) with gravimetric methods.
• Plant height measurement accuracy achieved a mean absolute error of 0.018 m.
• Data transmission reliability was 98.7% across 2920 scheduled attempts.
• The system enabled early water stress detection 24–48 hours before visible symptoms.
• Precision irrigation led to 15% water savings.
• The energy-autonomous design allowed for continuous operation for over 180 days on battery backup when solar charging was unavailable.
• CWSI values were generally below 0.3 during high-rainfall periods (March-June) and peaked around 0.5 in drier months (July-October).
• Irrigation was triggered when soil moisture dropped below 15% by weight, the underground water table stabilized at or below 1.50 m depth, and CWSI exceeded 0.35.
• The overall system operational uptime was 99.5%.
• Average daily energy consumption was 24 Wh, maintaining the battery state of charge above 70%.

Contributions

• Developed a novel energy-autonomous IoT architecture that unifies environmental monitoring, plant health assessment, and precision irrigation, addressing a gap where most solutions focus on either monitoring or irrigation control separately.
• Implemented a hierarchical power management system for extended off-grid operation, significantly improving energy autonomy compared to existing systems.
• Integrated multi-parameter decision-making, including CWSI-based stress detection, with multiple sensing modalities (environmental, soil, groundwater, and plant biometrics) and edge computing for real-time insights.
• Provided accessible mobile and web interfaces for farm managers, enhancing data-driven decision-making for sustainable sugarcane cultivation.

Funding

• Grant No. 10922/9211
• Financial assistance provided by the Sugarcane & By-products Development Research & Training Institute of Khuzestan.

Citation

@article{زاده2026Energyautonomous,
  author = {زاده, سامان آبدانان مهدی and Moalezadeh, Jamal Mohammadi and Abin, Amirabbas},
  title = {Energy-autonomous IoT-based wireless sensor networking architecture for plant health monitoring and precision irrigation in sugarcane},
  journal = {Smart Agricultural Technology},
  year = {2026},
  doi = {10.1016/j.atech.2026.101800},
  url = {https://doi.org/10.1016/j.atech.2026.101800}
}