Abstract
In automatic aerator-control systems used for shrimp farming, the dissolved oxygen (DO) measurement is one of the crucial parts since it affects both the quantity and quality of the product yield. It goes without saying that the more accurate the DO sensor, the more expensive it is. In this paper, we propose a technique for accuracy improvement of the DO measurement of a low-cost sensor by applying the Kalman filtering with an autoregressive model (AR). This work aims to minimize the difference between DO values read from the accurate sensor and those from less accurate sensors. Based on the standard Kalman filtering algorithm, data obtained from one low-cost sensor together with an AR of order 1 are used in the prediction stage, and data obtained from another sensor are used in the measurement update stage. Experimental results show that this technique can improve the measurement accuracy between approximately \(10\%\) and \(19\%\).
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Acknowledgment
This work was collaborative research between the National Electronics and Computer Technology Center (NECTEC) and the Aquaculture Product Development and Services (AAPS) laboratory of the National Center of Genetic Engineering and Biotechnology (BIOTEC), Thailand. The authors would like to express their sincere gratitude to Dr. Sage Chaiyapechara and his colleagues for domesticating the whiteleg shrimps in the experiments.
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Karnjana, J. et al. (2019). Improving Accuracy of Dissolved Oxygen Measurement in an Automatic Aerator-Control System for Shrimp Farming by Kalman Filtering. In: Omar, S., Haji Suhaili, W., Phon-Amnuaisuk, S. (eds) Computational Intelligence in Information Systems. CIIS 2018. Advances in Intelligent Systems and Computing, vol 888. Springer, Cham. https://doi.org/10.1007/978-3-030-03302-6_13
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