Version 1
: Received: 3 September 2024 / Approved: 3 September 2024 / Online: 4 September 2024 (18:14:37 CEST)
How to cite:
Kunapinun, A.; Fairman, W.; Wills, P. S.; Hanisak, D.; Ouyang, B. Advancing Seaweed Cultivation: Integrating Physics Constraint in Machine Learning for Enhanced Biomass Forecasting in IMTA Systems. Preprints2024, 2024090308. https://doi.org/10.20944/preprints202409.0308.v1
Kunapinun, A.; Fairman, W.; Wills, P. S.; Hanisak, D.; Ouyang, B. Advancing Seaweed Cultivation: Integrating Physics Constraint in Machine Learning for Enhanced Biomass Forecasting in IMTA Systems. Preprints 2024, 2024090308. https://doi.org/10.20944/preprints202409.0308.v1
Kunapinun, A.; Fairman, W.; Wills, P. S.; Hanisak, D.; Ouyang, B. Advancing Seaweed Cultivation: Integrating Physics Constraint in Machine Learning for Enhanced Biomass Forecasting in IMTA Systems. Preprints2024, 2024090308. https://doi.org/10.20944/preprints202409.0308.v1
APA Style
Kunapinun, A., Fairman, W., Wills, P. S., Hanisak, D., & Ouyang, B. (2024). Advancing Seaweed Cultivation: Integrating Physics Constraint in Machine Learning for Enhanced Biomass Forecasting in IMTA Systems. Preprints. https://doi.org/10.20944/preprints202409.0308.v1
Chicago/Turabian Style
Kunapinun, A., Dennis Hanisak and Bing Ouyang. 2024 "Advancing Seaweed Cultivation: Integrating Physics Constraint in Machine Learning for Enhanced Biomass Forecasting in IMTA Systems" Preprints. https://doi.org/10.20944/preprints202409.0308.v1
Abstract
Monitoring seaweed growth rates and biomass is crucial for optimizing harvest strategies in aquaculture systems. While such a task can be performed manually on a small farm, such as the Integrated Multi-Trophic Aquaculture (IMTA) system at Harbor Branch Oceanographic Institute at Florida Atlantic University (HBOI), a commercial farm will have to rely on the automated sensor to perform such a task. This study introduces an advanced LSTM-based approach for forecasting seaweed growth and biomass. Utilizing a combination of real and synthetic data, LSTM models are trained and evaluated for their predictive performance. Synthetic sensor data was generated using mathematical equations that simulate realistic aquaculture conditions, with added noise to reflect sensor variability. 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 non-linear regression models for predicting seaweed growth and biomass. The results showed that the LSTM model trained with a loss function under physics constraint, combining MSE and physical laws, outperformed models trained with MSE alone, achieving a significantly lower error in predicting seaweed growth. The variation trend of the predicted biomass from the network matched well with the sensor measurement after moving average preprocessing, demonstrating the robustness of the proposed technique in handling noisy sensor data. This study highlights the potential of integrating machine learning with physical models to optimize seaweed cultivation and support sustainable aquaculture practices.
Keywords
Integrated Multi-Trophic Aquaculture (IMTA); seaweed growth prediction; Long Short-Term Memory (LSTM); loss function under physics constraint; deep learning; synthetic data generation; sensor data augmentation; aquaculture optimization; biomass estimation; environmental monitoring; Robotic System
Subject
Environmental and Earth Sciences, Oceanography
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
