## 6 Conclusions  
This study was undertaken to detect mallards based on acoustic information. We developed a prototype system comprising two sets of microphone arrays. In all, we used 64 microphones installed on our originally designed and assembled sensor mounts. For the benchmark evaluation, we obtained three acoustic datasets in an outdoor environment. For the first experiment, the angular resolutions were evaluated using a single microphone array. An error accumulation tendency was demonstrated as the angle increased from the front side of the microphones. For the second experiment, the sound sources were estimated at 12 positions with 10 m grids in a square area. The accumulated position errors were affected by the frontal orientation settings of the two microphone arrays. For the third experiment, sound sources were at 23 positions with 5 m grids in a rectangular area. To minimize blind positions, the two microphone arrays were installed with their front sides facing diagonally. Although the error increased in the diagonal direction because of the effect of the angular resolution of shallow angles, the positional errors were reduced compared to the second experiment. These experimentally obtained results revealed that the proposed system demonstrated adequate accuracy for application to rice-duck farming. As future work aimed at practical use, we expect to improve our microphone array system in terms of miniaturization and waterproofing to facilitate its practical use for remote farming. For the problem of missing sound sources on the diagonal line between the two microphone arrays, we must consider superposition approaches using probability maps. We would like to distinguish mallard calls from noises such as the calls of larks, herons, and frogs in paddy fields. Moreover, for the protection of the ducks, we expect to combine acoustic information and visual information to detect and recognize the natural enemies of mallards. For this task, we plan on developing a robot that imitates natural enemies such as crows, kites, and weasels. We expect to improve the resolution accuracy of multiple mallard sound sources. Furthermore, we would like to discriminate mallards from crows and other birds and predict the time series mobility paths of mallards using a DL-based approach combine with state-of-the-art backbones. Finally, we would like to actualize the efficient protection of mallards by developing a system that notifies farmers of predator intrusion based on escape behavior patterns. This is because physical protection of mallards, such as electric fences and nylon lines around over paddy fields, requires a significant effort from farmers. Conceptualization, H.M.; methodology, K.W. and M.N. ; software, S.N. ; validation, K.W. and M.N. ; formal analysis, S.Y. ; investigation, S.Y. ; resources, K.W. and M.N. ; data curation, S.N. ; writing--original draft preparation, H.M.; writing--review and editing, H.M.; visualization, H.W. ; supervision, H.M.; project administration, K.S. ; funding acquisition, H.M. All authors have read and agreed to the published version of the manuscript. This research was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Numbers 17K00384 and 21H02321. **Institutional Review Board Statement:** Not applicable. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Experiment** & **Axis** & **0.3 m** & **0.6 m** & **0.9 m** & **1.2 m** \\ \hline B & \(x\) & 58.3\% & 75.0\% & 83.3\% & 91.7\% \\ B & \(y\) & 75.0\% & 75.0\% & 75.0\% & 83.3\% \\ C & \(x\) & 71.9\% & 81.3\% & 87.5\% & 87.5\% \\ C & \(y\) & 65.6\% & 84.4\% & 87.5\% & 90.6\% \\ \hline Mean & & 67.7\% & 78.9\% & 83.3\% & 88.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Estimation accuracies within grids up to the third neighborhood. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Datasets described as the results of this study are available upon request to the corresponding author.