Abstract
This paper presents an Internet of Things (IoT) system using K Nearest Neighbors Machine Learning Model for selection fish species by analyzing a fish data set. For storing real time data from used sensors, we used a cloud server. We make a dynamic website for giving information of various fish species living in an aquatic environment. This website is connected with cloud server; anyone can easily watch it on a web application. Therefore, they can easily decide what should follow the next step, which kinds of fish are surviving in the water. For constructing the proposed IoT system, we utilized 5 sensors including mq7, ph, temperature, ultrasonic and turbidity. These sensors are connected with an Arduino Uno. The real time data of water environment using sensor is obtained in the cloud server as a csv format file. In this study, we have utilized a server of thingspeak. The end user of fish farming can monitor easily remotely using the proposed IoT system.
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Islam, M.M., Uddin, J., Kashem, M.A., Rabbi, F., Hasnat, M.W. (2021). Design and Implementation of an IoT System for Predicting Aqua Fisheries Using Arduino and KNN. In: Singh, M., Kang, DK., Lee, JH., Tiwary, U.S., Singh, D., Chung, WY. (eds) Intelligent Human Computer Interaction. IHCI 2020. Lecture Notes in Computer Science(), vol 12616. Springer, Cham. https://doi.org/10.1007/978-3-030-68452-5_11
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