## 2 Materials and Methods
### Drinking Behavior Detection
Traditional depth sensing technologies, such as LiDAR or depth cameras (i.e., Kinect), are often expensive and require specialized equipment and maintenance [29]. By contrast, RGB cameras are relatively affordable, widely available, and easy to install, making them a practical solution for commercial poultry houses [9]. Furthermore, the combination of RGB imagery with DAM reduces the need for specialized depth sensing hardware, significantly lowering implementation and maintenance costs [30; 31]. ### Evaluation of Applying the DAM to Detect Drinking Behavior

Fifty images containing drinking areas were used to evaluate the performance of DAM in detecting drinking behavior. The average depth of the drinking lines was approximately 2.63 m, with the range of chickens drinking water being between 2.57 m and 2.68 m. Based on the predefined disparity range, we covered chicken's drinking activity when their peaks peck the drinking nipples [32]. This interaction causes the depth of the chicken's head and beak to align closely with the depth of the drinking line. The total number of chickens in the ROI is 504, with 430 identified as true positives and 35 as false negatives, resulting in an accuracy of 92.3%. In our study, DAM successfully detected chickens within the ROI, with the orange-colored areas representing the chickens' body parts that fell within the predefined disparity range (Figure 5. The model automatically identified the chickens' heads and backs, whose disparity values closely aligned with those of the drinking line, demonstrating its capability to precisely detect drinking behavior. This also corroborates the reasonableness of using a predefined disparity range based on the chickens' head and back positions along the drinking line [33]. Compared to previous studies, which either used a roughly designed ROI to count the number of birds in the drinking area or detected drinking behavior through classification based on a behaviorally segmented dataset, DAM provided a more practical approach [34]. For example, in Figure 5, if we do not use DAM and rely solely on the ROI, the total number of chickens drinking would be counted as nine. However, after applying DAM to compare the disparity of chickens with the drinking line, we successfully excluded four false detections, accurately counting only the chickens that were actually drinking. With only the ROI, the detection accuracy was 55.56%. After applying DAM to exclude the false detections, the accuracy improved to 100%. DAM's detection-based method allowed for the precise identification of chickens actually drinking water, whereas simpler ROI designs may miscount non-drinking chickens or those simply passing through the area. Classification-based approaches, on the other hand, require segmenting drinking chickens before comparing them to other behaviors, leading to higher computational costs. Figure 5: The Depth Anything Model (DAM) for monitoring drinking behavior: (**A**) original image, and (**B**) detected actual drinking behavior. Although DAM demonstrated practical effectiveness in detecting drinking behavior with greater precision, certain challenges remain. One issue is false detection when a chicken's tail, rather than its head, falls below the drinking line [35]. To address this, three potential solutions were considered. The first is to analyze areas where this tail-under-waterline phenomenon occurs frequently and use the \(y\)-axis of the picture to exclude irrelevant detections [36]. The second approach involves collecting more data on the average false detection range and comparing it to true detections, enabling a more accurate predefined range to balance detection accuracy [37]. The third alternative is to detect chicken heads specifically, but this would require higher-resolution images and the use of different-colored nipple connectors to avoid confusion with the color of the chickens' heads. A robotics camera system can also provide a viable solution for detecting actual drinking behavior, particularly when using a robotic dog.