Pseudorandom Encoded-light for Evaluating Biomass (PEEB): a Robust COTS Macroalgal Biomass Sensor for the Integrated Multi-Trophic Aquaculture (IMTA) System

BIG DATA IV: LEARNING, ANALYTICS, AND APPLICATIONS(2022)

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摘要
This article presents the experimental study of a biomass sensor to monitor the growth of macroalgae (seaweeds) in the Integrated Multi-Trophic Aquaculture (IMTA) at Harbor Branch Oceanographic Institute at Florida Atlantic University. Pseudorandom Encoded-light for Evaluating Biomass (PEEB) utilizes the measurements from a sequence of encoded light flashes to quantify the seaweed biomass. Such configuration ensures the sensor provides robust automated data acquisition under different ambient conditions and biomass densities. This data will be used to support a machine learning-based prediction biomass model, critical in any commercial-scale IMTA farm. A PEEB sensor based on an improved design has been developed based on an earlier feasibility study. The design of such a system and the initial tests at the macroalgal seaweed cultivation raceway in the HBOI IMTA system are discussed.
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integrated multi-trophic aquaculture, macroalgae biomass, biomass quantitation, optical sensor, machine learning, internet of things, aquaculture
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