## 5 Conclusions  
In this study, we have proposed a systematical way to analyze the user requirements of the AIoT information system for intelligent fish farming. Furthermore, the techniques to represent physical objects as digital twins have been discussed. The contributions of this work are (1) we proposed a methodology of a DT-based AIoT system design for precision aquaculture; (2) we integrated the proposed modified AHP as the evaluation methodology of DT architectures comprising the AIoT information system; (3) we implemented and applied the prototype system to monitor fish farms; (4) we designed the power supply and data communication system for offshore cages; and (5) we implemented the cloud-based AI functions to solve the issues of smart caging. Further studies would focus on risk management by implementing more decision-making and alarm functionalities.  
Conceptualization, H.-Y.L. and S.-C.C.; methodology, H.-Y.L, S.-C.C. and N.A.U.; software, S.-S.L, N.A.U. and C.-T.H.; validation, H.-Y.L, N.A.U. and S.-C.C; formal analysis, S.-S.L, S.-C.C. and N.A.U.; investigation, C.-T.H., S.-C.C. and N.A.U.; resources, H.-Y.L, S.-S.L, and C.-T.H.; data curation, H.-Y.L., S.-S.L. and C.-T.H.; writing\(-\)original draft preparation, H.-Y.L, S.-C.C. and N.A.U.; writing\(-\)review and editing, H.-Y.L., N.A.U. and C.-T.H.; visualization, H.-Y.L., S.-S.L. and N.A.U.; supervision, S.-C.C., S.-S.L. and C.-T.H.; project administration, S.-C.C.; funding acquisition, S.-C.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Science and Technology Council, Taiwan, under grant number NSTC 111-2221-E-019 -068 -MY3 and by Fisheries Agency, Council of Agriculture, Taiwan, under grant number 111 AS-6.2.1-FA-F6.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & \multicolumn{6}{c}{**AIoT Requirements Analysis Parameters**} \\ \cline{2-9}  & **(a)** & **(b)** & **(c)** & **(d)** & **(e)** & **(f)** & **(g)** \\ \hline [PERSON] and [PERSON] [49] & Water quality inspection & LoRa & Issues detection and alerting & Fish diseases detection & Web-based dashboard & 198 & 16 \\ \hline [PERSON] et al. [50] & Water quality inspection & Wifi & Issues detection and alerting & Fish growth status & Web-based dashboard & 16 & 34 \\ \hline [PERSON] et al. [51] & Water quality inspection & LoRa & Issues detection and alerting & x & Web-based dashboard & 673 & 26 \\ \hline [PERSON] et al. [3] & Multi-mode sensors suggestion & LoRA/4G/5G & Live data monitoring & Production planning & Data visualization & 1 & 77 \\ \hline [PERSON] et al. [52] & Underwater camera & x & \begin{tabular}{c} Computer \\ vision-based fish behavior analysis \\ \end{tabular} & x & x & 5 & 183 \\ \hline [PERSON] et al. [53] & Water quality sensors, RGB Camera & x & \begin{tabular}{c} Deep 1 \\ earning-based fish behavior analysis \\ \end{tabular} & x & x & 40 & 117 \\ \hline [PERSON] and [PERSON] [4] & Multi-mode sensor data & x & Fish behavior analysis & Production planning & \begin{tabular}{c} Visualization, \\ dashboard \\ \end{tabular} & 28 & 14 \\ \hline [PERSON] et al. [1] & Sonar, acoustic data, optical camera, ROV & x & Machine Learning-based models & \
\begin{tabular}{c} Models for farm operation \\ \end{tabular} &
\begin{tabular}{c} Visualization, \\ dashboard \\ \end{tabular} & 267 & 71 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Summary of findings regarding the construction of the AIoT for intelligent fish farming with columns (a) physical objects; (b) network communication; (c) basic service model; (d) fish farm data application; (e) user interface; (f) number of readers; (g) number of papers reviewed.