SMART IRRIGATION SYSTEM USING IOT AND LSTM FOR OPTIMAL WATER MANAGEMENT

Author

Farley Y. Ruiz, University of Texas at ArlingtonFollow

Graduation Semester and Year

Fall 2025

Language

English

Document Type

Thesis

Degree Name

Master of Science in Electrical Engineering

Department

Electrical Engineering

First Advisor

Yichen Zhang

Abstract

This thesis presents the design and implementation of a smart irrigation system that combines Internet of Things hardware with a Long Short-Term Memory (LSTM) neural network for predictive soil moisture management. The goal is an affordable and reliable solution that uses real-time sensor data and environmental data to schedule irrigation before the substrate moisture drops below its target range. The system integrates soil moisture, temperature, humidity, and sensors on an Arduino Nano that communicates wirelessly with a Raspberry Pi. The Raspberry Pi runs a Python/Flask backend that collects and processes data, executes the LSTM model, and serves a secure web dashboard for live visualization and manual override. The model is trained on a tomato dataset containing climate, irrigation, and weather records. A decision layer converts the model’s amount depth into actuator time via hydraulics mm to mL by wetted area, mL to seconds by effective flow and applies physically consistent guardrails based on soil storage balance and evapotranspiration. Results show that the controller, being operated continuously on a Raspberry Pi with low latency, maintains target moisture while reducing unnecessary watering. Because the hydraulics mapping is parameterized by area and flow, the approach is reproducible and portable across different pot sizes, fields, and pump configurations

Keywords

Smart irrigation, Internet of Things, Long Short-Term Memory, LSTM, Neural Networks, Time series forecasting, Automated irrigation

Disciplines

Artificial Intelligence and Robotics | Signal Processing | Systems and Communications

License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.

Recommended Citation

Ruiz, Farley Y., "SMART IRRIGATION SYSTEM USING IOT AND LSTM FOR OPTIMAL WATER MANAGEMENT" (2025). Electrical Engineering Theses. 399.
https://mavmatrix.uta.edu/electricaleng_theses/399