Prediction Model based on Bagging and Boosting Ensemble Technique for Decision Support System of Autonomous Smart IIoT Smart Aquaponic System

Abstract

The aqua-agriculture food sector, challenged by climate change, exploited biodiversity, food insecurity, water crisis, and global pandemic, urgently needs the sustainability transition and more sustainable practices. Sustainability transitions aim to transform current patterns of production and consumption into sustainable ones while enhancing the ecological and economic situation. This agenda is much possible with the integration of IIoT and AI/ML into the aqua agriculture. In this study, the researchers integrated a Prediction Model based on Bagging and Boosting Ensemble Technique in the Decision Support System of an Autonomous Smart IIoT Smart Aquaponic System that autonomously monitored, controlled, and managed the aquaponic systems.