A Data-Driven Intelligent Management Scheme for Digital Industrial Aquaculture based on Multi-object Deep Neural Network
With the development of intelligent aquaculture, the aquaculture industry is gradually switching from traditional crude farming to an intelligent industrial model. Current aquaculture management mainly relies on manual observation, which cannot comprehensively perceive fish living conditions and wat...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-04-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023458?viewType=HTML |
| _version_ | 1797842337337966592 |
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| author | Yueming Zhou Junchao Yang Amr Tolba Fayez Alqahtani Xin Qi Yu Shen |
| author_facet | Yueming Zhou Junchao Yang Amr Tolba Fayez Alqahtani Xin Qi Yu Shen |
| author_sort | Yueming Zhou |
| collection | DOAJ |
| description | With the development of intelligent aquaculture, the aquaculture industry is gradually switching from traditional crude farming to an intelligent industrial model. Current aquaculture management mainly relies on manual observation, which cannot comprehensively perceive fish living conditions and water quality monitoring. Based on the current situation, this paper proposes a data-driven intelligent management scheme for digital industrial aquaculture based on multi-object deep neural network (Mo-DIA). Mo-IDA mainly includes two aspects of fish state management and environmental state management. In fish state management, the double hidden layer BP neural network is used to build a multi-objective prediction model, which can effectively predict the fish weight, oxygen consumption and feeding amount. In environmental state management, a multi-objective prediction model based on LSTM neural network was constructed using the temporal correlation of water quality data series collection to predict eight water quality attributes. Finally, extensive experiments were conducted on real datasets and the evaluation results well demonstrated the effectiveness and accuracy of the Mo-IDA proposed in this paper. |
| first_indexed | 2024-04-09T16:46:15Z |
| format | Article |
| id | doaj.art-d2eb69748b3c4fe196becb633e59fbe0 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-04-09T16:46:15Z |
| publishDate | 2023-04-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-d2eb69748b3c4fe196becb633e59fbe02023-04-23T01:28:54ZengAIMS PressMathematical Biosciences and Engineering1551-00182023-04-01206104281044310.3934/mbe.2023458A Data-Driven Intelligent Management Scheme for Digital Industrial Aquaculture based on Multi-object Deep Neural NetworkYueming Zhou0Junchao Yang1Amr Tolba 2 Fayez Alqahtani3Xin Qi4Yu Shen 51. National Research Base of Intelligent Manufacturing Services, Chongqing Technology and Business University, Chongqing 400067, China2. School of Artificial Intelligence, Chongqing Technology and Business University, Chongqing 400067, China3. Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia4. Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 12372, Saudi Arabia5. School of International Liberal Studies, Waseda University, Tokyo 169-8050, Japan1. National Research Base of Intelligent Manufacturing Services, Chongqing Technology and Business University, Chongqing 400067, ChinaWith the development of intelligent aquaculture, the aquaculture industry is gradually switching from traditional crude farming to an intelligent industrial model. Current aquaculture management mainly relies on manual observation, which cannot comprehensively perceive fish living conditions and water quality monitoring. Based on the current situation, this paper proposes a data-driven intelligent management scheme for digital industrial aquaculture based on multi-object deep neural network (Mo-DIA). Mo-IDA mainly includes two aspects of fish state management and environmental state management. In fish state management, the double hidden layer BP neural network is used to build a multi-objective prediction model, which can effectively predict the fish weight, oxygen consumption and feeding amount. In environmental state management, a multi-objective prediction model based on LSTM neural network was constructed using the temporal correlation of water quality data series collection to predict eight water quality attributes. Finally, extensive experiments were conducted on real datasets and the evaluation results well demonstrated the effectiveness and accuracy of the Mo-IDA proposed in this paper.https://www.aimspress.com/article/doi/10.3934/mbe.2023458?viewType=HTMLintelligent aquaculturemulti-object deep learningfish state managementwater quality monitoring |
| spellingShingle | Yueming Zhou Junchao Yang Amr Tolba Fayez Alqahtani Xin Qi Yu Shen A Data-Driven Intelligent Management Scheme for Digital Industrial Aquaculture based on Multi-object Deep Neural Network Mathematical Biosciences and Engineering intelligent aquaculture multi-object deep learning fish state management water quality monitoring |
| title | A Data-Driven Intelligent Management Scheme for Digital Industrial Aquaculture based on Multi-object Deep Neural Network |
| title_full | A Data-Driven Intelligent Management Scheme for Digital Industrial Aquaculture based on Multi-object Deep Neural Network |
| title_fullStr | A Data-Driven Intelligent Management Scheme for Digital Industrial Aquaculture based on Multi-object Deep Neural Network |
| title_full_unstemmed | A Data-Driven Intelligent Management Scheme for Digital Industrial Aquaculture based on Multi-object Deep Neural Network |
| title_short | A Data-Driven Intelligent Management Scheme for Digital Industrial Aquaculture based on Multi-object Deep Neural Network |
| title_sort | data driven intelligent management scheme for digital industrial aquaculture based on multi object deep neural network |
| topic | intelligent aquaculture multi-object deep learning fish state management water quality monitoring |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2023458?viewType=HTML |
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