Advancing Seaweed Cultivation: Integrating Physics Constraint in Machine Learning for Enhanced Biomass Forecasting in IMTA Systems

Advancing Seaweed Cultivation: Integrating Physics Constraint in Machine Learning for Enhanced Biomass Forecasting in IMTA Systems

Monitoring seaweed growth rates and biomass is crucial for optimizing harvest strategies in aquaculture systems. While such a task can be managed manually on a small farm with a few seaweed tanks, it is not feasible on a commercial farm with 1000s of tanks. To this end, an Internet of Things (IoT) b...

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Bibliographic Details
Main Authors: Alisa Kunapinun, William Fairman, Paul S. Wills, Dennis Hanisak, Bing Ouyang
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Remote Sensing
Subjects:
Integrated Multi-Trophic Aquaculture (IMTA)
seaweed growth prediction
Long Short-Term Memory (LSTM)
loss function with physics constraint
deep learning
synthetic data generation
Online Access:https://www.mdpi.com/2072-4292/16/23/4418

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https://www.mdpi.com/2072-4292/16/23/4418

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