Advancing Seaweed Cultivation Analytics: Integrating Polynomial Regression Models with IoT for Optimized Harvesting in an Integrated Multi-Trophic Aquaculture (IMTA) System

This study explores an innovative approach to optimizing seaweed cultivation within Integrated Multi-Trophic Aquaculture (IMTA) systems at Harbor Branch Oceanographic Institute (HBOI) through the development of advanced sensor technologies and computational models. Building on the foundation of the Pseudorandom Encoded Light for Evaluating Biomass (PEEB) sensor deployed at the seaweed tank in the HBOI IMTA system, we refine the process of biomass estimation by introducing a methodology that combines the Random Sample Consensus (RANSAC) algorithm for sensor data refinement and non-linear regression models for predicting seaweed growth and biomass. The proposed framework adopts RANSAC to filter out data outliers, and utilizes weekly non-linear regression analyses to predict seaweed biomass and optimize harvest timing. The results demonstrate the effectiveness of our polynomial regression model in estimating the daily-averaged seaweed biomass, and potential of sensor-based biomass estimation in complex aquatic environments. We discuss the impact of data quality on prediction accuracy, the challenges posed by limited sensor calibration, and the short duration of sensor deployment on model reliability. Our study contributes to the sustainable management of IMTA systems by providing a data-driven foundation for automated seaweed cultivation, emphasizing the critical role of advanced technologies in the future of aquaculture.