Water Quality Modelling (WQM) Using Machine Learning (ML) For Surface Modelling of Shrimp Pond Vehicles Using Smart Water Optimization (SWO)
DOI:
https://doi.org/10.25047/tefa.v3i1.6735Keywords:
IoT, Shrimp, Machine LearningAbstract
Indonesia is one of global major aquaculture producer. White leg shrimp is considered a superior commodity due to its growth resilience. Therefore, annual white leg production is increasing from 2018 at 932,698 tons. In regard to these findings, the shrimp farming sector appears to hold great promise. But the quality of the water, which constantly fluctuates, is a key factor in shrimp farming success. However, in line with united nation (UN) sustainable development goals (SDGs) 12 in which requiring optimization on food production. The farming environment such as temperature, pH, and the availability of phytoplankton—a natural source of shrimp food—all have an impact on the water quality. To guarantee ideal circumstances, these parameters have to stay inside predetermined bounds. Automated water quality control is critical to improve shrimp production efficiency. The main goal of this research is to apply a fuzzy algorithm to create surface modelling vehicle for shrimp ponds (SMV-SP) where utilize the same sensor as autonomous surface vehicles (ASVs), but instead of classifying the sensors based on coral reef monitoring, the reference is optimal water condition to support shrimp growth. A 92% accuracy rate is indicated by the test finding. The result confirms ReSMeV-SP is able to improve water quality thus enabling more efficient and enhanced yield of shrimp production.
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