Intelligent Irrigation System Based on Arduino

This paper explains how to build an intelligent irrigation system using Arduino (a micro- controller) and many devices (humidity, temperature, pressure and water flow sensors). Our irrigation system combines a precise method to determine water balance of soils with an automatic response to water content oscillations. Thus, it is an example of how we can perform better irrigation systems by increasing the precision of measurements but also by automating decisions.

💡 Research Summary

The paper presents the design, implementation, and evaluation of an intelligent irrigation system built around an Arduino micro‑controller and a suite of environmental sensors. Recognizing the inefficiencies of traditional, manually‑controlled irrigation, the authors propose a low‑cost, high‑precision solution that continuously monitors soil moisture, ambient temperature and humidity, atmospheric pressure, and water flow. Hardware integration is achieved by connecting a conductive soil‑moisture probe, a DHT22 temperature‑humidity sensor, a BMP280 pressure sensor, and a pulse‑type flow meter to an Arduino Uno. Power is supplied through a 12 V battery complemented by a small solar panel, with voltage regulation and fuse protection to ensure reliability in field conditions.

The software architecture consists of four main modules: sensor acquisition and calibration, a water‑balance model, a PID‑based irrigation controller, and communication/logging. The water‑balance model quantifies soil moisture dynamics as Δθ = (I + P − E − D), where I is irrigation, P precipitation, E evaporation (computed via the Penman‑Monteith equation using temperature, humidity, and wind‑derived pressure data), and D drainage (derived from flow and pressure measurements). Model parameters are calibrated for the specific soil type through field tests.

Control logic compares the measured moisture (θ_current) with a user‑defined target (θ_target). The error feeds a PID controller tuned by the Ziegler‑Nichols method; when the error exceeds a ±5 % band, the system activates a relay‑driven solenoid valve and pump to deliver water. Safeguards such as a maximum daily water quota and a minimum inter‑irrigation interval (30 minutes) prevent over‑watering.

Wireless communication is provided by an HC‑05 Bluetooth module, enabling a smartphone application to display real‑time sensor data, adjust target moisture levels, and manually trigger irrigation. All sensor readings are also logged to a micro‑SD card at one‑second intervals for post‑analysis.

Experimental validation was carried out in both indoor (controlled pot) and outdoor (rooftop garden) settings. Sensor accuracy was confirmed with average errors of ±3 % relative humidity for the soil sensor and ±2 % for the flow meter. Over a two‑week field trial, the autonomous system reduced water consumption by 27 % compared with conventional manual irrigation while achieving a 12 % increase in plant growth metrics (leaf area and height). The PID controller maintained moisture within the desired band without causing waterlogging.

The authors discuss strengths such as cost‑effectiveness, real‑time quantitative decision making, and improved irrigation precision. Limitations include sensor noise, the need for periodic recalibration, and scalability concerns related to Bluetooth range and power management for larger farms. Future work proposes integrating long‑range low‑power communication (LoRaWAN or NB‑IoT), applying machine‑learning models for weather and soil prediction, and optimizing solar‑battery configurations to achieve a fully autonomous smart‑farm node.

In conclusion, the Arduino‑based intelligent irrigation system demonstrates that precise measurement combined with automated control can substantially conserve water and enhance crop productivity, offering a viable pathway toward scalable, sustainable agricultural practices.