Traditional fish farming faces several challenges, including water pollution, temperature imbalance, feed, space, cost, etc. Biofloc technology in aquaculture transforms the manual into an advanced system that allows the reuse of unused feed by converting them into microbial protein. The objective of the research is to propose an IoT-based solution to aquaculture that increases efficiency and productivity. The article presented a system that collects data using sensors, analyzes them using a machine learning model, generates decisions with the help of Artificial Intelligence (AI), and sends notifications to the user. The proposed system has been implemented and tested to validate and achieve a satisfactory result.

In this paper, we present a regression framework involving several machine learning models to estimate water parameters based on hyperspectral data. Measurements from a multi-sensor field campaign, conducted on the River Elbe, Germany, represent the benchmark dataset. It contains hyperspectral data and the five water parameters chlorophyll a, green algae, diatoms, CDOM and turbidity. We apply a PCA for the high-dimensional data as a possible preprocessing step. Then, we evaluate the performance of the regression framework with and without this preprocessing step. The regression results of the framework clearly reveal the potential of estimating water parameters based on hyperspectral data with machine learning. The proposed framework provides the basis for further investigations, such as adapting the framework to estimate water parameters of different inland waters.