Engineering study on predictive functions of water quality in aquaponics systems

• Main Author: Thia, Terance Jin Leon
• Other Authors: Heng Kok Hui, John Gerard
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2023
• Subjects: Engineering::Mechanical engineering
• Online Access: https://hdl.handle.net/10356/166970
• Institution: Nanyang Technological University

Singapore faces dire challenges on attaining food independence where 90% of her food supply is imported and is unable to tackle this challenge through conventional means since Singapore is a small nation with little land available for agriculture. Additionally, Singapore faces a shrinking working population and ageing work force. The combination of aquaponics and agriculture automation could help address these challenges. Although there are some agriculture automation solutions on managing the health of crops available in the market, it is targeted towards medium to large scale aquaponics systems. As such, more can be done in the study of developing and/or researching of affordable solutions that provides the general public with personal aquaponics systems. This engineering study aims to develop an automated water quality management system that possesses the predictive function of determining a vital parameter without utilizing that parameter’s sensor. This study fulfills the aim by validation of sensor accuracy, data collection at an aquaponics farm, utilizing machine learning techniques, and integration of the data logging and predictive function to the developed water quality management system. The results of this study enabled the developed water quality system to be deployed with the advantage of cutting costs from sensor equipment and reduce man hours needed for managing water quality whilst maintaining an acceptable level of accuracy.