Survivability of Aqua Marine Products in Fish Ponds through Water Quality Evaluation Using Machine Learning Algorithm

Authors

  • Lester G. Loyola
  • Luisito L. Lacatan

Abstract

Water quality is considered as the most important factor in aquaculture production systems affecting fish health and performance.  Water quality can be considered good if it relates to what aquaculture wants and needs to survive and develop, which means that aquacultured farmers must be able to understand the water quality requirements of their cultured products to ensure their fast growth and survival. Different species of fish have different and particular aspects of water quality needs in which they can live, grow and reproduce.[1]–[5] It is, therefore, imperative for fish producers to make sure that the physical and chemical conditions of pond water remain, as much as possible, within the optimum or acceptable range of the fish under culture all the time. Fish may show poor development, erratic behavior, and signs of disease or parasite infestations outside of these acceptable ranges. Fish kill can occur in extreme cases or where poor conditions persist for extended periods of time.[1], [4], [6]–[8] Using water sampling to monitor water quality takes up time, while laboratory results do not show the current state of water in fish ponds, which is critical information needed by fish farmers. Water quality monitoring in fish ponds should be in real-time, analysis of water parameters must be done as soon as possible to ensure water quality and its acceptability for aquacultured products. The purpose of this study is to develop a system that can monitor water parameters in fish ponds analyze and evaluate these parameters to determine the suitability and survivability rate of aquacultured products base on water quality using machine learning algorithms regression tree and decision tree in accordance with the water quality requirements of cultured products. With the help of Arduino microcontroller device that uses IoT (Internet of Things) technology. By implementing this study, it was found out that the system effectively helps the fish farmers to manage and help maintain the water quality of their fish ponds it minimizes losses due to untimely solutions to water quality problems and promotes a healthy environment which helps increase the growth and survival of aquacultured products. It also increases the fish farmer harvest and income, which creates a positive impact on agricultural productivity in terms of fish farming.

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Published

2020-01-21

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Articles