## 1 Introduction  
In recent years, artificial intelligence has made significant strides, particularly in agricultural systems, where precision and efficiency are critical [1]. One major challenge in precision livestock farming is the monitoring of animal behavior and welfare, which are critical for ensuring comfort, reducing stress, and maintaining ethical husbandry practices. Separately, detecting health conditions and assessing physiological development are equally essential for enabling timely interventions and improving productivity [2]. While computer vision has driven innovations in this field, the growing volume of data from in-field sensors and cameras introduces complexities in data processing. Existing depth estimation methods in agriculture often rely on stereo vision systems, depth sensors (e.g., LiDAR, RGB-D cameras), or supervised deep learning models trained on small, annotated datasets. While these methods have demonstrated effectiveness in tasks such as plant phenotyping, orchard mapping, and fruit counting [3, 4], their application in poultry production--especially in cage-free environments--remains very limited. Most prior studies in poultry behavior monitoring focus on 2D image-based detection without leveraging depth information, primarily due to cost and complexity associated with depth sensors [5]. Furthermore, existing monocular depth estimation techniques require substantial labeled data and often suffer from reduced generalization when applied to new environments [6]. Few, if any, studies have investigated scalable depth estimation models using only RGB data for monitoring poultry behavior and facility conditions. This study addresses this gap by applying a general-purpose monocular depth estimation model to poultry production, eliminating the need for specialized depth sensors and enabling a broader range of behavior and operational insights in commercial cage-free systems. Building on this foundation, we introduce the Depth Anything Model (DAM), a novel solution for monocular depth estimation, designed for diverse and challenging agricultural scenarios [7]. Unlike traditional deep models, such as those used for citrus yield prediction, DAM emphasizes scalability and simplicity using a massive, automatically annotated dataset. In contrast, traditional models typically rely on smaller, manually labeled datasets. For example, a study employing red, green, and blue (RGB), Near-Infrared (NIR), and depth sensors for citrus yield estimation used 195 training images and 30 testing and validation images. The deep learning model, based on the AlexNet algorithm, required 2000 manually cropped samples of fruit and background for training [8]. Although these models can be effective, they often demand labor-intensive data preparation and are restricted by their smaller dataset size [9]. Additionally, traditional models tend to focus on specific tasks, such as detecting circular objects in images using algorithms like the Circular Hough Transform (for RGB and NIR images) or the CHOICE algorithm (for depth images) [10]. This task-specific approach limits their generalization capabilities. In contrast, DAM focuses on simplicity and scalability, leveraging a dataset of over 62 million unlabeled images. This expansive dataset enables the model to generalize across varying conditions, making it ideal for precision agriculture applications. It enhances adaptability and efficiency in real-world scenarios, such as monitoring livestock behavior or detecting anomalies in farming environments [11]. Although DAM demonstrates a strong zero-shot capability for depth prediction and can estimate depth information from monocular images, such as those captured with standard RGB cameras, eliminating the need for specialized depth sensors, its application in poultry science remains underexplored [12; 13; 14]. By relying solely on RGB cameras, DAM has the potential to significantly reduce costs associated with depth sensor technologies.