A remote wireless system for water quality online monitoring in intensive fish culture |
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| Institution: | 1. College of Information and Electrical Engineering, China Agricultural University, 17 Tsinghua East Road, Beijing 100083, PR China;2. Key Lab. of Agricultural Bio-environment Engineering of Ministry of Agriculture, China Agricultural University, Beijing 100083, PR China;1. Department of Bio-industrial Mechatronics Engineering, University of Chung-Hsing, Taichung, Taiwan, ROC;2. Department of Biomechatronic Engineering, University of Chiayi, Chiayi, Taiwan, ROC;1. School of Information Science & Engineering, Changzhou University, Changzhou 213164, China;2. Changzhou Technical Institute of Tourism & Commerce, Changzhou 213023, China;1. Institute of Mathematics and Informatics, Szent István University, Páter K. u.1, Gödöllő, H-2103, Hungary;2. Department of Physical and Applied Geology, Eötvös Loránd University, Pázmány Péter sétány 1/C, Budapest, H-1117, Hungary;1. SINTEF Ocean, 7465 Trondheim, Norway;2. NTNU Department of Engineering Cybernetics, 7491 Trondheim, Norway;3. KU Leuven, M3-BIORES, Kasteelpark Arenberg 30, bus 2456, 3001 Leuven, Belgium;4. School of BioSciences, University of Melbourne, Victoria 3010, Australia;5. Research Center for Experimental Marine Biology and Biotechnology—Plentziako Itsas Estazioa (PIE), University of the Basque Country UPV/EHU, 48620 Plentzia, Spain;6. Dept. of Graphic Design & Engineering Projects, Faculty of Engineering of Bilbao, University of the Basque Country UPV/EHU, 48013 Bilbao, Spain;7. University of New Hampshire, Department of Biological Sciences, Rudman Hall, Durham, NH 03824, USA;1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, PR China;2. Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing 100083, PR China;3. Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing 100083, PR China;4. National Innovation Center for Digital Fishery, China Agricultural University, Beijing 100083, PR China |
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| Abstract: | Water quality monitoring and forecasting plays an important role in modern intensive fish farming management. This paper describes an online water quality monitoring system for intensive fish culture in China, which combined web-server-embedded technology with mobile telecommunication technology. Based on historical data, this system is designed to forecast water quality with artificial neural networks (ANNs) and control the water quality in time to reduce catastrophic losses. The forecasting model for dissolved oxygen half an hour ahead has been validated with experimental data. The results demonstrate that multi-parametric, long-distance and online monitoring for water quality information can be accurately acquired and predicted by using this established monitoring system. |
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