## 2 Materials and Methods
### Drinking Behavior Detection
### Exploring the Application of DAM in Commercial Cage-Free Houses

To further explore the application of the DAM in cage-free egg production, we conducted tests using twenty images consisting of commercial cage-free eggs with both white and brown shells, which represent the two main types of cage-free eggs. DAM demonstrated its capability to detect egg disparities effectively. For brown eggs, the disparities were measured at 2.24, 4.96, and 8.25 (Figure 8). In the case of white eggs, the disparities were 7.76, 7.97, 8.65, 8.72, 12.35, 12.48, 13.40, 13.71, and 23.62 (Figure 9). All egg disparities

Figure 6: The disparity detected by DAM (Depth Anything Model). (**A**) original image, (**B**) detected image showing disparity in eggs. Figure 7: Clustered egg collection route based on disparity. within these categories were correctly predicted, indicating that DAM is effective in detecting egg depth even in commercial cage-free farming systems. This depth information can be utilized to estimate the actual egg depth, enabling egg-collecting robotics to optimize picking routes, thereby improving efficiency and reducing operational costs. ## 4 Conclusions

This study evaluated the Depth Anything Model (DAM) for monocular depth estimation using standard RGB images in poultry farming, with the goal of eliminating the

Figure 8: Disparity detected by DAM (Depth Anything Model) in brown-shelled cage-free eggs. (**A**) original image, (**B**) detected image showing disparity in eggs. Figure 9: Disparity detected by DAM (Depth Anything Model) in white-shelled cage-free eggs. (**A**) original image, (**B**) detected image showing disparity in eggs. need for depth sensors. The DAM was applied to monitor poultry behaviors--particularly detecting drinking patterns--and to manage tasks like tracking floor eggs. Additionally, the model's accuracy in detecting disparity was assessed using perch frame depth comparisons in cage-free environments. DAM showed robust depth prediction performance, accurately identifying drinking behavior and enabling optimized clustering for egg collection. Although the model performed well overall, challenges such as false detections during drinking behavior and occlusions were noted. Further research will focus on enhancing detection accuracy under more complex conditions, including higher bird densities and commercial-scale variations. This work demonstrates the potential of DAM to improve behavioral monitoring and route optimization in cage-free systems, laying the groundwork for advanced machine vision solutions in precision livestock farming. Methodology, X.Y. and L.C. ; Software, X.Y. ; Validation, X.Y. ; Formal analysis, X.Y. ; Investigation, X.Y., G.L., J.Z., B.P., A.D., S.D., R.B.B. and L.C. ; Resources, G.L. and L.C. ; Writing--original draft, X.Y., G.L., J.Z., B.P., A.D., S.D. and L.C. ; Supervision, L.C. ; Project administration, L.C. ; Funding acquisition, L.C. All authors have read and agreed to the published version of the manuscript. The study was sponsored by the Georgia Research Alliance (Venture Fund), Oracle America (Oracle for Research Grant, CPQ-2060433), and UGA Institute for Integrative Precision Agriculture. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. The authors declare no conflict of interest. ## References

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