A Data-Driven Intelligent Management Scheme for Digital Industrial Aquaculture based on Multi-object Deep Neural Network

Yueming Zhou; Junchao Yang; Amr Tolba; Fayez Alqahtani; Xin Qi; Yu Shen; Yueming Zhou; Junchao Yang; Amr Tolba; Fayez Alqahtani; Xin Qi; Yu Shen

doi:10.3934/mbe.2023458

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 6: 10428-10443. doi: 10.3934/mbe.2023458

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A Data-Driven Intelligent Management Scheme for Digital Industrial Aquaculture based on Multi-object Deep Neural Network

1.
National Research Base of Intelligent Manufacturing Services, Chongqing Technology and Business University, Chongqing 400067, China
2.
School of Artificial Intelligence, Chongqing Technology and Business University, Chongqing 400067, China
3.
Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia
4.
Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 12372, Saudi Arabia
5.
School of International Liberal Studies, Waseda University, Tokyo 169-8050, Japan

Received: 02 February 2023 Revised: 20 March 2023 Accepted: 26 March 2023 Published: 06 April 2023

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.
- Intelligent aquaculture,
- multi-object deep learning,
- fish state management,
- water quality monitoring
Citation: 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[J]. Mathematical Biosciences and Engineering, 2023, 20(6): 10428-10443. doi: 10.3934/mbe.2023458

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Abstract

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.

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