Multi node sensors for water quality monitoring towards precision aquaculture. Issue 1 (April 2021)

• Record Type: Journal Article
• Authors: Komarudin, M; Septama, H D; Yulianti, T; Yudamson, A; Hendri, J; Arafat, M A D
• Is Part Of: IOP conference series. Volume 739:Issue 1(2021)
• Publication Date: 2021-04
• Subjects: Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/739/1/012026
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: Shrimp culture is carried out by ponds in open areas, especially near coastal areas. The ponds water condition or water quality has a significant impact on the shrimp culture. There are also frequent problems among these shrimp ponds, such as crop failure caused by bad water quality. The water quality monitoring in shrimp ponds is often done manually by the farmer in periodical times. The water quality monitoring that is done manually tends to be impractical, requires high worker wages, and has a high human error rate. With the advances in the field of Information Technology, data may be retrieved through sensors and collected into a server. Then the data may be processed and visualized in order to support precision aquaculture using the Internet of Things (IoT). Precision Fish Farming (PFF) or precision aquaculture is a concept that applies control-engineering principles to aquaculture industries. The PPF concepts allow farmers to have the ability to monitor, control, and document biological processes in aquaculture farms. This research aims were to design and build a multi node sensor and master board to monitor water quality in real time using the prototyping method. The system consists of several sensors for monitoring temperature, pH, and salinity in shrimp ponds that are installed at each node. Nodes are actively sending data to the master board. This model is done to reduce the need for direct data access to the internet. The monitoring system is tested in PB Tunas Baru shrimps pond in order to check if the system may work properly. The sensor is set to retrieve pond water quality data every 5 minutes in a total 100 minute period. The result shows that the model works properly, and the means value of the total error rate for the salinity sensors, pH, and temperature sensors consecutively is 1.65%, 1.25%, and 0%. This information allows the farmers to maintain the water quality precisely in aim to produce high quality shrimp crops toward the precision aquaculture concepts.