Benito et al. (2025) Optimizing Water Efficiency in Urban Farming with an Automated Smart Drip Irrigation System

Identification

• Journal: QCU The Star
• Year: 2025
• Date: 2025-12-02
• Authors: Ian Benito, Sandy Daras, Rechelle Ann Intawon, Rachell Lacaba, M. Yupanqui Mendoza, Patrick Jeremie Supnet
• DOI: 10.64807/qecryn09

Research Groups

• College of Engineering, Quezon City University, Quezon City, Philippines

Short Summary

This study developed and evaluated an Automated Smart Drip Irrigation System for urban farming, demonstrating its ability to significantly reduce water consumption while maintaining or improving crop health and yield compared to traditional irrigation methods.

Objective

• To optimize water use in urban farming through the implementation of an Automated Smart Drip Irrigation System.
• To conduct a comparative analysis of water usage between conventional irrigation systems and the automated smart drip system, highlighting improvements in water conservation and crop yield.

Study Configuration

• Spatial Scale: A designated garden bed at Sharon Farm, Quezon City, Philippines.
• Temporal Scale: Two months of field testing.

Methodology and Data

• Models used:
  • Arduino Uno microcontroller as the main processing unit.
  • IPO (Input-Process-Output) model for conceptual framework.
  • Taguchi Design of Experiment (DOE) methodology (L9 orthogonal array) for optimizing operational parameters (irrigation time, soil moisture threshold, water volume).
  • Analysis of Variance (ANOVA) for statistical analysis of experimental results.
• Data sources:
  • Real-time data from capacitive soil moisture sensors.
  • Field testing observations: daily water consumption (liters), soil moisture levels (percent), crop health indicators (leaf color, growth rate, yield, chlorophyll content, plant height in meters).
  • Feedback from farm personnel.
  • IoT-based monitoring via a mobile application.

Main Results

• The Automated Smart Drip Irrigation System achieved water savings of up to 57.7% compared to traditional manual watering methods.
• The Taguchi DOE successfully identified optimal combinations of irrigation timing, soil moisture thresholds, and watering duration for efficient irrigation.
• Crop health indicators, such as leaf chlorophyll content and plant height, were consistent with or better than those in control plots, indicating no compromise on plant growth.
• The integration of IoT enabled remote monitoring and control, significantly reducing the need for manual labor.
• Statistical analysis (ANOVA) confirmed a significant difference between the tested factors (Time, Soil Moisture, Water Usage) and their impact on system performance, validating the system's robustness.

Contributions

• Provides a practical, scalable, and sustainable solution for enhancing water efficiency in urban farming.
• Quantitatively demonstrates significant water conservation (up to 57.7%) through an automated, sensor-driven drip irrigation system.
• Validates that increased water efficiency does not compromise crop health or yield, and can even improve them.
• Integrates IoT technology for remote monitoring and control, improving system responsiveness and reducing manual labor.
• Utilizes robust statistical methods (Taguchi DOE, ANOVA) to systematically optimize and validate system performance parameters.
• Aligns with Sustainable Development Goals (SDG) 11 (Sustainable Cities and Communities) and 12 (Responsible Consumption and Production) by promoting sustainable urban agriculture.

Funding

• This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
• Acknowledgment is given for personal support and provision of the pilot testing location and resources by the Catahan family.

Citation

@article{Benito2025Optimizing,
  author = {Benito, Ian and Daras, Sandy and Intawon, Rechelle Ann and Lacaba, Rachell and Mendoza, M. Yupanqui and Supnet, Patrick Jeremie},
  title = {Optimizing Water Efficiency in Urban Farming with an Automated Smart Drip Irrigation System},
  journal = {QCU The Star},
  year = {2025},
  doi = {10.64807/qecryn09},
  url = {https://doi.org/10.64807/qecryn09}
}