Academy & Industry Research Collaboration Center (AIRCC)

Volume 12, Number 13, July 2022

A Big Data Driven System to Improve Residential Irrigation Efficiency using Machine Learning and AI


Kai Segimoto1, Nelly Segimoto1 and Yu Sun2, 1USA, 2California State Polytechnic University, USA


California has been prone to drought; starting in 2011, there were 376 consecutive weeks of drought [1]. More effective tools are necessary to combat water scarcity, in particular in irrigation systems [3]. This paper designs an application to modify current water-saving techniques to create a more environmentally friendly irrigation system [2]. We developed a Big Data Driven System to Improve Residential Irrigation Efficiency. Our design uses the raspberry Pi controller based on an IoT system with a database connected to the cloud. We designed a mobile app to interact with the system and collect the data and a machine learning algorithm to analyze and generate recommendations based on the given data [4]. We applied our application to the irrigation systems of California Residents and conducted a qualitative evaluation of the approach. The results show that trend-based water saving techniques were effective in reducing water usage without sacrificing the health of the plants being irrigated.


Data mining, Cloud computing, Machine Learning, IoT system.