ARTICLE
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doi:10.20944/preprints202409.0308.v1
Subject:
Oceanography,
Environmental And Earth Sciences
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
Online: 4 September 2024 (18:14:37 CEST)