Online chicken carcass volume estimation using depth imaging and 3-D reconstruction

• Published in: Poultry science Vol. 103; no. 12; p. 104232
• Main Authors: Nyalala, Innocent, Jiayu, Zhang, Zixuan, Chen, Junlong, Chen, Chen, Kunjie
• Format: Journal Article
• Language: English
• Published: England Elsevier Inc 01.12.2024; Elsevier
• Subjects: 3-D reconstruction; chicken carcass; depth imaging; poultry grading; PROCESSING AND PRODUCT; volume estimation; chicken carcass; depth imaging; poultry grading; volume estimation; 3-D reconstruction
• ISSN: 0032-5791; 1525-3171; 1525-3171
• DOI: 10.1016/j.psj.2024.104232

Variability in the size of slaughtered chickens remains a longstanding challenge in the standardization of the poultry industry. To address this issue, we present a novel approach that uses volume as a grading metric for chicken carcasses. This innovative method, unexplored in existing studies, employs real-time data capture of moving chicken carcasses on a production line using Kinect v2 depth imaging and 3-D reconstruction technologies. The captured depth images are processed into point clouds followed by 3-D reconstruction. Volume is calculated from the reconstructed models using the surface integration method, and additional 2-D and 3-D features are extracted as input parameters for machine learning models. Multiple regression models were evaluated, with the bagged tree model demonstrating superior performance, achieving an R² value of 0.9988, RMSE of 5.335, and ARE of 2.125%. Furthermore, our method showed remarkable efficiency with an average processing time of less than 1.6 seconds per carcass. These results indicate that our novel approach fills a critical gap in existing automated grading methodologies by offering both accuracy and efficiency. This validates the applicability of depth imaging, 3-D reconstruction, and machine learning for estimating chicken carcass volume with high precision, thereby enabling a more comprehensive, efficient, and reliable chicken carcass grading system.