Application of Regression Analysis to Achieve a Smart Monitoring System for Aquaculture

Abstract

The consumption awareness of people in recent years has increased, with food safety becoming more and more important. While non-toxic products can be achieved by avoiding using too much antibiotics to control growth factors in a water environment, the measurement tools for dissolved oxygen on the market are very expensive and a great economic burden to fishermen. Thus, the purpose of this study is to design more economical measurement modules and algorithms for monitoring ponds. The research collected pond data through Oxidation-Reduction Potential (ORP), pH and temperature sensors, used regression analysis to infer Dissolved Oxygen (DO) by ORP and pH, and employed a real-time pond monitoring data map to figure out pond conditions. Compared with traditional equipment, findings show our approach reduces costs by about 20%, and increases production capacity and output value.