What is it about?
This study introduces a deep learning model called the Temporal Fusion Transformers (TFT), designed to predict nitrate levels in aquaponic systems. Aquaponic systems combine modern hydroponics with aquaculture, where ammonia produced by fish is converted into nitrate by bacteria, creating a soilless farming ecosystem. The research aims to forecast nitrate levels using sensor data to enhance the automation of these systems.
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Why is it important?
Fluctuations in ammonia levels within aquaponic systems can affect nitrate production and, consequently, crop yields. Manual control of these systems is prone to human error, but AI-powered Internet of Things (IoT) solutions can reduce human intervention, creating more scalable and efficient food production environments. This study addresses a critical issue in food production by offering a solution that could significantly improve the automation of aquaponic systems.
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This page is a summary of: Temporal fusion transformer-based prediction in aquaponics, The Journal of Supercomputing, June 2023, Springer Science + Business Media,
DOI: 10.1007/s11227-023-05389-8.
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