A deep learning-enabled IoT framework for early hypoxia detection in aqua water using light weight spatially shared attention-LSTM network.

Dissolved oxygen (DO) is a critical factor in maintaining healthy aquatic ecosystems, including aquaculture ponds. Low DO levels can lead to hypoxia conditions, which are detrimental to fish health and productivity. To deal with this issue, we intend for a smart monitoring system that predicts hypoxia conditions due to low DO levels in aquaculture ponds. The proposed system collects water quality data using Internet of things (IoT) devices and segments it into different categories based on water quality parameters, with a particular focus on low DO levels. By detecting hypoxia conditions early, fish farmers can take corrective measures to prevent fish mortality and improve fish health. To achieve this, our proposed system uses a lightweight Spatially Shared Attention Long Short-Term Memory (SSA-LSTM) model that captures both temporal and spatial dependencies of DO content in water, enabling accurate prediction of hypoxia conditions. Our model outperforms traditional LSTM models and other existing state-of-the-art models, achieving 99.8% accuracy. The proposed system provides a reliable and efficient solution to monitor hypoxia conditions in aquaculture systems and help fish farmers make informed decisions for optimal fish health and productivity. [ABSTRACT FROM AUTHOR]