Smart Irrigation Control Using IOT Sensors and Machine Learning for Optimized Water Management

Abstract

With the rapid advancement of digital technologies and the growing global demand for sustainable agricultural practices, traditional irrigation systems are undergoing a transformative evolution. Agriculture, being one of the most water-intensive sectors, faces increasing pressure to optimize water usage while ensuring maximum crop yield. Smart irrigation systems—leveraging sensors, microcontrollers, and predictive algorithms—have emerged as a solution to this problem, offering automated water management based on real-time soil and environmental data. In this project, an intelligent irrigation system was developed that integrates Internet of Things (IoT) components with a machine learning (ML) model to automate irrigation decisions. A hardware prototype was constructed using a soil moisture sensor and a controller capable of activating a water pump based on environmental conditions. The system supports both automatic and manual modes, allowing flexibility in operation depending on the context and user preference. On the software side, a supervised ML model—specifically a K-Nearest Neighbors (KNN) classifier—was trained using historical sensor data (soil moisture, temperature, and humidity) to predict whether irrigation should be turned ON or OFF. The dataset was preprocessed and normalized, and the model achieved approximately 66% accuracy in its predictions. The prototype successfully demonstrated automated irrigation behavior that responds intelligently to varying soil conditions, conserving water and eliminating the need for manual monitoring. This project not only showcases the feasibility of combining ML and IoT for smart agriculture but also presents a practical framework for developing low-cost, scalable irrigation systems that can be tailored to small-scale and large-scale farms alike. The integration of real-time sensing with predictive analytics holds significant promise in addressing water scarcity and improving farming efficiency. Future enhancements may include more complex models, weather integration, and remote access via mobile applications to further increase system robustness and adaptability.

Introduction

The text describes a smart irrigation system designed for efficient water management in agriculture using IoT sensors and machine learning (ML). The system automates irrigation based on real-time soil moisture, temperature, and humidity readings, helping farmers conserve water, save time, and optimize crop yield.

Key Points:

1. Objective:
   Develop an intelligent, automated irrigation system that predicts when to turn water pumps ON/OFF using IoT sensors and an ML classifier (K-Nearest Neighbors).

2. Components:

   • ESP32 Microcontroller: Central controller with Wi-Fi/Bluetooth connectivity.

   • Sensors: Soil moisture sensor, DHT11 temperature and humidity sensor.

   • Actuators: 12V DC pump or solenoid valve controlled via TIP122 transistor, buzzer for alerts.

   • Display & Indicators: 0.96" OLED for sensor readings, LEDs for mode and Wi-Fi status.

   • Supporting Electronics: Resistors, diodes, capacitors, voltage regulators (7805), and transistors (BC547).

3. Functionality:

   • Sensing Unit: Continuously monitors soil and atmospheric conditions.

   • Processing Unit: ESP32 processes sensor data, either using threshold logic or an ML model to make irrigation decisions.

   • Machine Learning Integration: KNN model predicts irrigation state (ON/OFF) based on historical environmental data.

   • Modes: Automatic (ML-driven) and manual override.

4. Benefits:

   • Optimizes water usage, reduces manual labor, and improves irrigation efficiency.

   • Predictive ML-based decisions enable adaptive irrigation according to real-time conditions.

   • Scalable and suitable for precision agriculture applications.

This system demonstrates a practical IoT + ML solution for intelligent, autonomous irrigation, improving resource efficiency and crop productivity.

Conclusion

This project successfully demonstrates the integration of IoT hardware with a machine learning-based decision system for smart irrigation. By leveraging real-time data from soil moisture, temperature, and humidity sensors, the system effectively predicts the irrigation requirement and automates the pump’s ON/OFF control using a K-Nearest Neighbors (KNN) classifier. The model achieved an accuracy of approximately 66%, with higher reliability in detecting irrigation needs than withholding them, aligning with agricultural risk priorities. The simplicity of the model and its compatibility with low-cost hardware like the ESP32 make it a promising solution for resource-constrained environments. Additionally, the user-friendly binary prediction approach ensures accessibility even for non-technical users. Overall, the project demonstrates how machine learning, when paired with embedded systems, can offer practical, automated solutions for sustainable farming and water conservation.

References

[1] Allen, R. G., Pereira, L. S., Raes, D., & Smith, M., 1998. Crop evapotranspiration — Guidelines for computing crop water requirements (FAO Irrigation and Drainage Paper No. 56). Food and Agriculture Organization (FAO). — Standard reference for evapotranspiration and water-requirement calculations used in irrigation design and discussion of climatic inputs. FAOHome. [Accessed on: 22-Dec-2025]. [2] Del-Coco, M., et al., 2024. Machine Learning for Smart Irrigation in Agriculture, Information (MDPI) — Recent survey focusing on ML approaches for irrigation scheduling, useful for literature review and justification of ML use. MDPI. [Accessed on: 22-Dec-2025]. [3] Younes, A., Elamrani Abou Elassad, Z., et al., 2024. The application of machine learning techniques for Smart Irrigation Systems: A systematic literature review, Smart Agricultural Technology / Elsevier — Systematic review of ML models used in smart irrigation (KNN, RF, SVM, ANN) and their empirical performance. Good for comparing model choices. ScienceDirect. [Accessed on: 22-Dec-2025]. [4] Tace, Y., et al., 2022. Smart irrigation system based on IoT and machine learning, (Elsevier / ScienceDirect) — Implementation-focused paper showing an IoT+ML pipeline and performance metrics; helps support architecture and methodology choices. ScienceDirect. [Accessed on: 22-Dec-2025]. [5] Espressif Systems, ESP32-WROOM datasheet (module technical documentation) — Official datasheet and technical reference for the ESP32 module used as the microcontroller/edge device in the hardware. Use this for pinout, power, and wireless specs. Espressif Systems. [Accessed on: 22-Dec-2025]. [6] Espressif Systems, ESP-IDF Programming Guide — Official programming/deployment guide for ESP32 (useful if you flash models or telemetry code to the ESP32). Espressif Systems. [Accessed on: 22-Dec-2025]. [7] DHT11 Technical Data Sheet (manufacturer / distributor PDF) — Datasheet describing electrical characteristics, timing, and measurement accuracy for the DHT11 temperature & humidity sensor used in the project. Use when describing sensor accuracy and calibration limitations. Mouser Electronics+1. [Accessed on: 22-Dec-2025]. [8] DFRobot (Capacitive Soil Moisture Sensor SKU SEN0193) — Product documentation describing capacitive sensing principles, corrosion resilience and recommended wiring (good for hardware / sensing-unit section). wiki.dfrobot.com. [Accessed on: 22-Dec-2025]. [9] SSD1306 OLED Controller — SSD1306 datasheet (Adafruit / S Systech) for the 0.96? I²C OLED modules. Use this for display interfacing details in hardware description. Adafruit+1. [Accessed on: 22-Dec-2025]. [10] Firebase Realtime Database — Official Firebase docs describing realtime data sync, security rules, and client APIs (useful if your system logs sensor data to the cloud or supports remote control). Firebase+1. [Accessed on: 22-Dec-2025]. [11] scikit-learn documentation — KNeighborsClassifier reference (algorithm description, parameters) and preprocessing (StandardScaler / MinMaxScaler): use these as authoritative technical references for model selection, scaling rationale, and code-level implementation. Scikit-learn+2Scikit-learn+2. [Accessed on: 22-Dec-2025]. [12] LastMinuteEngineers — “Interfacing Capacitive Soil Moisture Sensor with Arduino” (tutorial) — Practical wiring and code examples for capacitive soil sensors and microcontrollers; good for hardware wiring diagrams and code snippets in the Methodology. lastminuteengineers.com. [Accessed on: 22-Dec-2025]. [13] Recent implementations & student prototypes on Smart Irrigation + KNN (examples & proceedings) — several conference and journal implementations (e.g., AIP/ICITMSEE 2024, IJCRT 2021) — good to cite as closely-related prior work showing KNN and RF comparisons in irrigation tasks. Representative sources: Reddy et al. (2024/2025) and IJCRT (2021). pubs.aip.org+1. [Accessed on: 22-Dec-2025].

Copyright

Copyright © 2025 Upangshu Basak, Swarnendu Chakraborty. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.