Mishra et al. (2026) AQUASAVVY: An Intelligent IoT-Based Smart Irrigation System for Efficient Water Management

Identification

• Journal: International Journal of Advancement and Innovation in Technology and Research
• Year: 2026
• Date: 2026-01-01
• Authors: Amay Mishra, Aditi Kumrawat, Akshat Karode, Jayesh Kushwa, Nisha Rathi, Ashish Anjana
• DOI: 10.63844/ijaitr.v3.i1.2026.80-86

Research Groups

• Acropolis Institute of Technology & Research, Indore, India

Short Summary

This paper develops and validates AQUASAVVY, an Internet of Things (IoT)-based smart irrigation system that monitors real-time soil and environmental conditions to automate water supply, achieving significant water savings (up to 50%), reduced labor, and improved crop yield compared to traditional methods.

Objective

• To design, implement, and evaluate an IoT-based smart irrigation system (AQUASAVVY) that monitors real-time soil moisture, temperature, and humidity to automate water supply, aiming to optimize water usage, reduce labor, and improve crop productivity compared to conventional irrigation methods.

Study Configuration

• Spatial Scale: 1-acre (approximately 4047 square meters) test plot; designed for small-holder farmers.
• Temporal Scale: Continuous monitoring (data collected every 5-15 minutes); 24-hour monitoring cycles; 30-day field test; 6-month water savings analysis; 5-year cost-benefit analysis.

Methodology and Data

• Models used: Threshold-Based Logic for automated irrigation control; future scope for Machine Learning (ML)-based predictive models (e.g., Random Forest, Decision Trees, Neural Networks).
• Data sources:
  • Sensors: Capacitive/Resistive Soil Moisture Sensor, DHT22 Temperature & Humidity Sensor, YF-S201B Water Flow Sensor.
  • Microcontroller: Arduino Uno or NodeMCU (ESP32).
  • Connectivity: Wi-Fi module.
  • Cloud Platform: ThingSpeak, Blynk, or custom MQTT server for data storage, visualization, and remote monitoring.
  • Data Parameters: Soil moisture (%), air temperature (°C), air humidity (%), water flow (liters).

Main Results

• Water Savings: Achieved 50% water savings compared to manual irrigation (from 0.45 m³/day to 0.225 m³/day) and 41% compared to timed sprinklers.
• Water Efficiency: Demonstrated 88% water efficiency, significantly higher than manual irrigation (55%).
• Labor Reduction: Reduced labor requirements by 96% (from 14 hours per week to 0.5 hours per week).
• Crop Yield: Resulted in an 18.5% increase in crop yield due to optimal soil moisture management.
• System Reliability: Maintained 99.2% uptime over a 30-day field test period with an average response time of 4.5 seconds (from threshold detection to water delivery).
• Economic Viability: Achieved a break-even Return on Investment (ROI) within 10 months and projected 50% cost reduction over 5 years (₹36,500 in savings).
• Environmental Impact: Contributed to a 60% reduction in carbon dioxide (CO₂) emissions (from 4.32 GJ/year to 1.872 GJ/year energy consumption), 60% reduction in nutrient runoff, 75% reduction in soil erosion risk, and 50% reduction in groundwater extraction.

Contributions

• Developed and experimentally validated AQUASAVVY, a comprehensive IoT-based smart irrigation system that integrates real-time sensor data, automated control, and cloud-based monitoring.
• Quantified substantial improvements in water conservation (50% savings), operational efficiency (96% labor reduction), and agricultural productivity (18.5% crop yield increase) compared to traditional methods.
• Demonstrated the economic viability of smart irrigation with a rapid 10-month ROI and significant long-term cost savings for farmers.
• Provided a robust framework for sustainable farming by significantly reducing environmental impacts such as water consumption, CO₂ emissions, nutrient runoff, and soil erosion.
• Laid the groundwork for future enhancements including machine learning integration for predictive irrigation, multi-zone control, and solar-powered solutions.

Funding

• Acropolis Institute of Technology and Research provided facilities and support.
• Prof. Nisha Rathi and Prof. Ashish Anjana (CSIT Department AITR) provided support.

Citation

@article{Mishra2026AQUASAVVY,
  author = {Mishra, Amay and Kumrawat, Aditi and Karode, Akshat and Kushwa, Jayesh and Rathi, Nisha and Anjana, Ashish},
  title = {AQUASAVVY: An Intelligent IoT-Based Smart Irrigation System for Efficient Water Management},
  journal = {International Journal of Advancement and Innovation in Technology and Research},
  year = {2026},
  doi = {10.63844/ijaitr.v3.i1.2026.80-86},
  url = {https://doi.org/10.63844/ijaitr.v3.i1.2026.80-86}
}