Development of realtime surface modeling vehicle for shrimp ponds (ReSMeV-SP)

Abstract

Abstract The global frozen shrimp export market in 2018 was recorded at US $ 17.2 billion or around Rp.232.2 trillion (exchange rate Rp.13,500/US$). Indonesia is one of the main exporters of frozen shrimp in the global market, to be exact, in fourth place after India, Ecuador and Vietnam. The export value of Indonesian frozen shrimp last year, according to Worldtopexports.com, reached US$ 1.3 billion or Rp.17.55 trillion. Indonesia’s frozen shrimp market share reaches 7.8%. Indonesia’s frozen shrimp export market includes, among others, the United States, Japan and European Union countries. Based on these data, shrimp farming is a promising sector, but shrimp farming is very much determined by water quality. Water quality in shrimp farming always changes. Many factors influence changes in water quality. Among them are the amount of dissolved oxygen, availability of phytoplankton as natural shrimp feed, the state of shrimp biomass, PH and temperature. These factors in the water must be within the standard boundary rank. Therefore, to achieve the production efficiency of the shrimp industry, it is necessary to automate water quality control. This study aims to develop the Real-time Surface Modeling Vehicle for Shrimp Ponds (ReSMeV-SP) Using Fuzzy Algorithm. The tool produced by this study (ReSMeV-SP) will use the same sensor as the ASV but with a different class classification, the research is no longer referring to coral reef standards but in this study the reference is the optimal water conditions to support the growth of Vename Shrimp. The test results show an accuracy rate of 92%, the hope is that the tools developed by the research will be able to realize precise Vename Shrimp farming, so that production efficiency and increased shrimp production can be achieved.