IoT and machine learning approach for the determination of optimal freshwater replenishment rate in aquaponics system

Abstract

Conventional aquaponics conserve water used in aquaponics whereas the literature suggests a certain level of freshwater replenishment or freshwater exchange for good water quality, fish and plant wellbeing, and the overall productivity of the system. This paper deals with the determination of an optimal freshwater replenishment rate for a standard aquaponics system. IoT devices and sensors were used for this project data collection. This paper used linear regressions and ensemble methods to determine the optimal rate of periodic water replenishment to maintain the water quality parameters that determine the yield and productivity of aquaponics systems. Cubic spline and Lagrange interpolation were applied to raw and simulated data. Results were evaluated and compared using statistical error estimation approaches. The best model amongst the investigated machine learning models was gradient boost with an optimal replenishment rate of 19L per week and a water quality of 4.86 for an aquaponic tank of 100 L capacity. The error estimations were a Mean Squared Error of 0.0224, Mean Absolute Error of 0.1137, Root Mean Squared Error of 0.1499, and R2 of 0.7208. This was within 1% of the value obtained from raw and interpolated data using a polynomial regression. These findings suggest that the water quality of an aquaponics system can be maintained at the desired optimal level with a weekly 19% water replenishment, thereby contributing to the improvement of productivity and resource efficiency.