Development of smart aquaculture farm management system using IoT and AI-based surrogate models

Abstract

Due to low labor participation by young adults and an aging agricultural population, Taiwan and the rest of the world are facing labor shortages in agriculture, which will affect aquaculture production. The proposed system is intended primarily for solving the problems faced by the aquaculture farming sector in Taiwan by designing a smart IoT-based fish monitoring and control system equipped with different IoT devices to enable real-time data collection; so that fishpond water-quality conditions and other system parameters can be readily monitored, adjusted, and assessed remotely. To predict the growth of the California Bass fish, this study also develops a deep learning model (DL) that correlates the different parameters of the smart aquaculture system. Bayesian optimization-based hyper-parameter tuning was employed to find the optimal DL model configuration to produce accurate predictions on the given experimental data set. The optimal model produces an R 2 value of 0.94 and a mean square error of 0.0015, demonstrating the applicability of the model to predict the desired output. Based on the results of the experiments, the DL model can be incorporated into the autonomous feeding system, reducing the amount of leftover feed. Thus, aquaculture based on the artificial intelligence of things (AIOT) can assist fish farmers in intelligently controlling and managing different fishpond equipment remotely and assist aquaculture operators in performing professional aquaculture, lowering the industry's entry barrier, and promoting aquaculture. • Developed an AIoT-based smart aquaculture farm management system that enables remote control and monitoring. • A DL model is developed to correlate the different parameters of a fish-feeding system and predict the growth of fish. • Bayesian optimization is utilized to find the most suitable DL model that will produce the highest R2 and mean square error.