Senthilkumar (2025) A cloud-connected iot model for crop monitoring and automated irrigation management

Identification

• Journal: International Journal of Zoology and Applied Biosciences
• Year: 2025
• Date: 2025-12-01
• Authors: P. Senthilkumar
• DOI: 10.55126/ijzab.2025.v10.i06.sp090

Research Groups

• PERI Institute of Technology, Chennai, Tamil Nadu, India
• PERI College of Arts and Science, Chennai, Tamil Nadu, India
• PERI College of Physiotherapy, Chennai, Tamil Nadu, India
• PERI College of Pharmacy, Chennai, Tamil Nadu, India
• PERI College of Nursing, Chennai, Tamil Nadu, India

Short Summary

This study developed a cloud-connected Internet of Things (IoT) model for real-time crop monitoring and automated irrigation management. Experimental deployment demonstrated a significant reduction in water usage (28–45%) and improved crop health compared to manual irrigation methods.

Objective

• To develop and evaluate a cloud-connected IoT model for crop monitoring and automated irrigation management aimed at improving water efficiency, optimizing crop health, and enhancing ease of field supervision.

Study Configuration

• Spatial Scale: 3 meter x 3 meter agricultural plot
• Temporal Scale: 30 days of field observation

Methodology and Data

• Models used: Threshold-based control algorithm (soil moisture < 30% activates irrigation), time-based override for irrigation duration, simple moving average for data validation.
• Data sources: Capacitance-based soil moisture sensors, DHT11/DHT22 temperature-humidity sensors, LDR/light sensors. Data transmitted wirelessly via LoRa/Wi-Fi to a cloud platform (e.g., Firebase, AWS IoT Core, Things Board).

Main Results

• The IoT system achieved a 98% data packet success rate for transmitting environmental parameters to the cloud.
• Automated irrigation resulted in a 28–45% reduction in water usage compared to manual irrigation, along with improved water distribution and elimination of over-irrigation.
• The system demonstrated a low latency of 2–5 seconds between cloud decision and microcontroller response for valve activation.
• Fields managed by the system showed consistent soil moisture levels, reduced plant stress, and higher uniformity in plant height and leaf greenness, indicating improved crop vigor.
• The cloud dashboard provided real-time charts, historical trend analysis, and remote control capabilities, enhancing usability for farmers.

Contributions

• Presents a scalable and cost-effective digital agriculture solution suitable for small- and large-scale farming environments.
• Validates the potential of IoT-powered agriculture in enhancing sustainability and addressing labor and resource challenges.
• Establishes a foundation for future intelligent farming innovations, including predictive irrigation, machine learning integration, and autonomous crop management systems.

Funding

• This study received no specific funding from public, commercial, or not-for-profit funding agencies.

Citation

@article{Senthilkumar2025cloudconnected,
  author = {Senthilkumar, P.},
  title = {A cloud-connected iot model for crop monitoring and automated irrigation management},
  journal = {International Journal of Zoology and Applied Biosciences},
  year = {2025},
  doi = {10.55126/ijzab.2025.v10.i06.sp090},
  url = {https://doi.org/10.55126/ijzab.2025.v10.i06.sp090}
}