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AI-Based Smart Irrigation System Using IoT and Machine Learning for Precision Agriculture

Author

Listed:
  • Suraksha Kardile

    (Department of Electronics and Telecommunication Engineering, Dr. Babasaheb Ambedkar Technological University, Raigad, Maharashtra, India)

  • Sanjay Nalbalwar

    (Department of Electronics and Telecommunication Engineering, Dr. Babasaheb Ambedkar Technological University, Raigad, Maharashtra, India)

  • Tejas Mahagaonkar

    (Department of Electronics and Telecommunication Engineering, Dr. Babasaheb Ambedkar Technological University, Raigad, Maharashtra, India)

Abstract

Efficient water management is a cornerstone of sustainable precision agriculture, yet traditional irrigation often suffers from over-irrigation due to static scheduling. This paper presents a robust IoT-enabled smart irrigation framework that leverages the ESP32 microcontroller and a suite of environmental sensors (soil moisture, DHT22, rain, and water flow) integrated with machine learning for dynamic decision-making. Unlike threshold-based systems, our approach utilizes a Random Forest classifier to predict irrigation needs based on multivariate environmental inputs, achieving a prediction accuracy of 94.2%. Real-time data is synchronized with the Thing Speak cloud platform, enabling remote monitoring and data-driven insights. Experimental results demonstrate a 35% reduction in water consumption compared to conventional methods while maintaining optimal soil moisture levels for crop growth.

Suggested Citation

  • Suraksha Kardile & Sanjay Nalbalwar & Tejas Mahagaonkar, 2026. "AI-Based Smart Irrigation System Using IoT and Machine Learning for Precision Agriculture," International Journal of Latest Technology in Engineering, Management & Applied Science, RSIS International, vol. 15(7), pages 1124-1127, August.
    • Handle: RePEc:bjf:ijltem:v:15:y:2026:i:7:a:94
    DOI: 10.51583/IJLTEMAS.2026.150700090
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