# A Smart Sensing System of Water Quality and Intake Monitoring for Livestock and Wild Animals
## 5 Discussion
Furthermore, the duration and life are unknown as it is an ongoing research. However, we are closely monitoring the condition of operation. Data from the RFID systems, motion sensor-based cameras, and water level monitors are analyzed to make sure that the real drinking events is detected and the animal ID is detected. A valid drinking event happens only when the water level monitor records water consumption. We recorded several pictures from the camera showing no animals at the drinking station and some RFID events without images from the camera. Both cases have no water consumption. Therefore, we believe the cross-validation could demonstrate the accuracy of the system. A more accurate measurement is to deploy a continuous monitoring surveillance system to verify the drinking events manually, which we are planning in the future experiments. Therefore, more future work could be performed to enhance system functionality and performance. Currently, the system can detect the drinking event including the animal ID, the water consumption, and temperature of the water. The current system does not have the capability to process data from individual sensors to identify sensor failures. For example, the system cannot verify the detected animal ID from the RFID system using the image data. The water turbidity sensor was not enabled and would be added in the later experiments. The system can be further improved by future work from different aspects. For example, the communication system from the remote site could be replaced by satellite communication or dedicated long-range telecommunication. Currently, the data were stored locally in the SD card as the remote site was not covered by the cellular data service. We are trying to deploy data links at the remote site. Once the data link is deployed, we can create a wireless sensor network. At this moment, the remote site has no wireless communication means (cellular or internet). Therefore, the current system does not address the central failure problem. In future work, we plan to add more backup data storage in the system. The animal identification system would benefit from a machine \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **This Work** & **[43]** & **[44]** & **[45]** & **[46]** \\ \hline Type & Academic & Academic & Academic & Industrial & Industrial \\ \hline Year & 2021 & 2020 & 2019 & 2018 & 2020 \\ \hline \multirow{2}{*}{Method} & Image + RFID + & \multirow{2}{*}{RFID + Water flow sensor} & \multirow{2}{*}{RFID + Accelerometer} & \multirow{2}{*}{Flow meter} & \multirow{2}{*}{RFID} \\  & Depth sensor & & & & \\ \hline Animal ID & Yes & Yes & Yes & No & Yes \\ \hline Indoor Confinement Only & No & Yes & Yes & Yes & Yes \\ \hline Potential Wildlife Usage & Yes & No & No & N/A & No \\ \hline \hline \end{tabular}
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Table 2: Comparison between this work and recently published systems. learning-based animal face recognition system in order to alleviate the problem of error detections. Water quality refers to water temperature and water turbidity. More water sensors could be included in the system such as pH sensors specific chemical sensors, for example, sensors to detect Sulfur, Calcium, and Iron, which are the most common problems in New Mexico waters. Weight scales can also be implemented at the drinking station to measure and record the animal's weight during the drinking event. The improved sensing system may be connected to control systems to perform better water management. For instance, shutting the water supply off if water contamination is detected due to acid rain or other reasons. The system may be combined with weather systems to have an early preparation of weather changes such as heavy rains or high temperatures. Furthermore, the drinking behavior data such as visit time and visit duration of the domestic animal could be applied to train machine learning algorithms to identify the abnormal features of animal health status [47; 48; 49; 50; 51]. Early detection and intervention could help save costs related to animal health issues.