Article: Smart water management for precision irrigation in agriculture Journal: International Journal of Environment and Waste Management (IJEWM) 2026 Vol.39 No.4 pp.420 - 432 Abstract: More than ever, creative solutions are required to fight water scarcity and improve resource efficiency in agriculture. Using the ESP32 Devkit-C microcontroller, this project demonstrates a smart water management system designed for precision farming. The TensorFlow Lite system integrates machine learning methods, particularly support vector machines (SVM), to provide localised and real-time irrigation decision-making. The hardware setup consists of DHT11 and soil moisture sensors connected to a relay module that manages the water pump. Based on the state of the environment at the time, the ESP32 makes intelligent irrigation decisions by interpreting sensor data. Effective edge AI processing is made possible by pre-trained machine learning models. By supplying water precisely when and where it is required, the system can increase crop yields by about 20% and improve irrigation efficiency by up to 25%. The smart water management system addresses the dynamic challenges of water resource management and promotes sustainable farming practices by integrating hardware, machine learning, and real-time decision-making to offer a scalable and flexible solution for contemporary precision agriculture. Inderscience Publishers - linking academia, business and industry through research

Title: Smart water management for precision irrigation in agriculture

Authors: Rupali Atul Mahajan; Rajesh Dey; Gitanjali Shinde; Basanta Kumar Bhuyan; Kalpana Bhuyan; Sharada Desai

Addresses: Vishwakarma Institute of Technology, Pune, India ' Gopal Narayan University, Bihar, India ' Vishwakarma Institute of Technology, Pune, India ' Manav Rachna International Institute of Research and Studies, Haryana, India ' Seemanta Engineering College, Jharpokharia, Mayurbhanj, India ' Vishwakarma Institute of Technology, Pune, India

Abstract: More than ever, creative solutions are required to fight water scarcity and improve resource efficiency in agriculture. Using the ESP32 Devkit-C microcontroller, this project demonstrates a smart water management system designed for precision farming. The TensorFlow Lite system integrates machine learning methods, particularly support vector machines (SVM), to provide localised and real-time irrigation decision-making. The hardware setup consists of DHT11 and soil moisture sensors connected to a relay module that manages the water pump. Based on the state of the environment at the time, the ESP32 makes intelligent irrigation decisions by interpreting sensor data. Effective edge AI processing is made possible by pre-trained machine learning models. By supplying water precisely when and where it is required, the system can increase crop yields by about 20% and improve irrigation efficiency by up to 25%. The smart water management system addresses the dynamic challenges of water resource management and promotes sustainable farming practices by integrating hardware, machine learning, and real-time decision-making to offer a scalable and flexible solution for contemporary precision agriculture.

Keywords: edge AI; internet of things; IoT; support vector machine; SVM; TensorFlow Lite.

DOI: 10.1504/IJEWM.2026.153408

International Journal of Environment and Waste Management, 2026 Vol.39 No.4, pp.420 - 432

Received: 12 Sep 2024
Accepted: 01 Feb 2025
Published online: 07 May 2026 *

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Title: Smart water management for precision irrigation in agriculture

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