Version 1
: Received: 11 January 2024 / Approved: 11 January 2024 / Online: 11 January 2024 (12:02:02 CET)
How to cite:
Lan, H.; Cheng, S.; Lu, H.; Chang, C.; Li, S.; Huang, C. A Novel Process-Based Digital Twin for Intelligent Fish Feeding Management Using Multi-Mode Sensors and Smart Feeding Machine. Preprints2024, 2024010927. https://doi.org/10.20944/preprints202401.0927.v1
Lan, H.; Cheng, S.; Lu, H.; Chang, C.; Li, S.; Huang, C. A Novel Process-Based Digital Twin for Intelligent Fish Feeding Management Using Multi-Mode Sensors and Smart Feeding Machine. Preprints 2024, 2024010927. https://doi.org/10.20944/preprints202401.0927.v1
Lan, H.; Cheng, S.; Lu, H.; Chang, C.; Li, S.; Huang, C. A Novel Process-Based Digital Twin for Intelligent Fish Feeding Management Using Multi-Mode Sensors and Smart Feeding Machine. Preprints2024, 2024010927. https://doi.org/10.20944/preprints202401.0927.v1
APA Style
Lan, H., Cheng, S., Lu, H., Chang, C., Li, S., & Huang, C. (2024). A Novel Process-Based Digital Twin for Intelligent Fish Feeding Management Using Multi-Mode Sensors and Smart Feeding Machine. Preprints. https://doi.org/10.20944/preprints202401.0927.v1
Chicago/Turabian Style
Lan, H., Shih-Yu Li and Cheng-Ting Huang. 2024 "A Novel Process-Based Digital Twin for Intelligent Fish Feeding Management Using Multi-Mode Sensors and Smart Feeding Machine" Preprints. https://doi.org/10.20944/preprints202401.0927.v1
Abstract
The utilization of smart IoT devices, commonly referred to as digital twins, is aimed at the digitalization of human knowledge within aquaculture processes. This involves the incorporation of cutting-edge technologies, including information-based management with big data and modeling, to automate machinery and gain comprehensive insights into the aquaculture environment and fish farm conditions. The ultimate objective is to empower farmers to make informed decisions, furnishing them with objective data to enhance their capacity in monitoring and controlling the various factors impacting fish production. As a result, farming decisions can be fine-tuned to enhance fish health and optimize farm output. In the context of large and modern aquaculture farms, technological innovation becomes imperative to automate processes, minimize labor requirements, and streamline fish feeding operations. Remarkably, the literature currently offers limited discussions on the digital transformation of aquaculture through the application of digital twin methodologies. A prior study underscores the critical influence of factors such as market prices and fish survival rates on the profitability of offshore caging culture. In this study, we embark on an analysis of the prerequisites for establishing a digital twin infrastructure tailored to intelligent fish feeding management. This infrastructure is designed to facilitate the integration of technology and data-driven decision-making, ultimately enhancing the efficiency of fish feeding processes. The proposed architecture for the fish feeding digital twin encompasses various digital twin components, encompassing water quality forecasting, fish population assessment, fish metrics estimation, fish feed prediction, and evaluation of fish feeding intensity. Furthermore, we optimize the daily fish feeding process through reinforcement learning algorithms. Finally, we implement a cloud-based AIoT system that provides the runtime environment for executing digital twins and controlling our intelligent fish feeding machinery. Experimental findings underscore the efficacy of the proposed digital twin system in significantly improving traditional fish feeding processes, notably in terms of reducing food costs and labor requirements.
Keywords
digital twin; smart fish feeding machine; multi-mode sensors; fish feeding process; AI aquaculture transformation.
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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