Integration and evaluation of a low-cost intelligent system and its parameters for monitoring three-dimensional features of broiler chickens

Effective monitoring systems are crucial for improving poultry management and welfare. However, despite the enhanced analytics provided by 3D systems over 2D, affordable options remain limited due to unresolved design and algorithm challenges. The objective of this study was to develop a low-cost intelligent system to monitor the 3D features of poultry. The system consisted of data storage, a mini-computer, and electronics housed in a plastic box, with an RGB-D camera externally connected via USB for flexible installation. Python scripts on a Linux-based Robotic Operating System (ROS Noetic) were developed to automatically capture 3D data, transfer it to storage, and notify a manager when storage is full. Various 3D cameras, installation heights (2.25, 2.50, 2.75, and 3.00 m), image resolutions, and data compression settings were tested using a robotic vehicle in a 1.2 m × 3.0 m pen to simulate broiler movement in controlled environments. Optimal configurations, based on the quality of 3D point clouds, were tested in several broiler trials including one containing 1,776 Cobb 500 male broiler chickens. Results showed that the integrated L515 camera provided clearer features and superior 3D point cloud quality at 2.25 m, capturing an average of 1641 points per frame. Additionally, data compression reduced RGB frame storage by 75%, enabling efficient long-term storage without compromising data quality. During broiler house testing with 1,776 Cobb 500 male broilers, the system demonstrated stable and reliable operation, recording 1.65 TB of data daily at 15 FPS with a 20 TB hard drive, allowing for 12 consecutive days of uninterrupted monitoring. Among object detection models tested, YOLOv8m (a medium-sized version of the YOLO version 8 model) outperformed other models by achieving a precision of 89.2% and an accuracy of 84.8%. Depth-enhanced modalities significantly improved detection and tracking performance, especially under challenging conditions. YOLOv8m achieved 88.2% detection accuracy in darkness compared to 0% with RGB-only data, highlighting the advantage of integrating depth information in low-light environments. Further evaluations showed that incorporating depth modalities also improved object detection in extreme lighting scenarios, such as overexposure and noisy color channels, enhancing the system's robustness to environmental variations. These results demonstrated that the system was well-suited for accurately capturing 3D data across diverse conditions, providing reliable detection, tracking, and trajectory extraction. The system effectively extracted 3D walking trajectories of individual chickens, enabling detailed behavioral analysis to monitor health and welfare indicators. The system, costing approximately $1,221, integrates cost-effective hardware with a scalable software architecture, enabling precision monitoring in large-scale operations. By reducing storage costs to $28 per day and compressing data without losing critical details, the system is well-suited for practical deployment in poultry farms. The outlined evaluation and customization process ensures that this framework can be adapted to other agricultural or industrial applications, paving the way for intelligent and scalable monitoring systems. These advancements provide a robust foundation for improving animal management, enhancing productivity, and addressing welfare concerns in precision agriculture.