Published May 6, 2026 | Version v1
Preprint Open

A Smart Precision Irrigation System Based on Neural Networks and Internet of Things

Authors/Creators

  • 1. ROR icon Arab International University

Description

AgriFlow AI is a smart irrigation setup that helps farmers cut down on water waste. It relies on low‑cost sensors and a Raspberry Pi to keep an eye on soil moisture, temperature, humidity, and the latest weather forecast. All that data gets sent to a neural network running in the cloud, which then decides the best watering schedule. The system can turn the valve on and off entirely on its own, but there’s also a web dashboard where the farmer can check the field in real time and jump in whenever they want. During testing, the model reached an R² of 0.92, missed the mark by only about 2 minutes on average, and ended up using around 30% less water than a regular timer‑based system.

This work was conducted at Arab International University (AIU), Syria. The official website of the university is: https://www.aiu.edu.sy

Files

An Al-Driven Precision Irrigation System Using Neural Networks, loT Sensors, and Automated Control.pdf

Files (835.0 kB)

Additional details

References

  • [1] L. E. Luiz, G. Fialho, and J. Teixeira, "Predicting Soil Moisture Using Neural Networks," Procedia Computer Science, 2024.
  • [2] C. Yeung, R. Bunker, and K. Fujii, "A Machine Learning Framework for Evapotranspiration with Weather APIs," Global Journal of Artificial Intelligence, 2023.
  • [3] S. Zaveri, S. Tiwari, and L. Teli, "Smart Irrigation Decision Making Process," International Journal on Recent and Innovation Trends in Computing and Communication, 2020.
  • [4] P. Kashyap, S. Kumar, and A. Singh, "LSTM-based Soil Moisture Forecasting for Smart Irrigation," Computers and Electronics in Agriculture, vol. 185, pp. 106–115, 2021.
  • [5] O. Adeyemi, I. Grove, and S. Peets, "Reinforcement Learning for Closed-Loop Irrigation Control," Biosystems Engineering, vol. 175, pp. 82–95, 2018.
  • [6] M. Garcia, L. Parra, and J. Rocher, "Edge-AI for Real-Time Irrigation Decision Making," IEEE Access, vol. 10, pp. 2345–2358, 2022.
  • [7] A. Goap, D. Sharma, A. K. Shukla, and C. R. Krishna, "An IoT based smart irrigation management system using Machine learning and open source technologies," Computers and Electronics in Agriculture, vol. 155, pp. 41–49, 2018, doi: 10.1016/j.compag.2018.09.034.
  • [8] T. Barhoum, "SETF: A Structured Engineering Thesis Framework for Artificial Intelligence, Software Engineering, and Robotics," Zenodo, Apr. 2026, doi: 10.5281/zenodo.19686845.