Mechanisms of Machine Vision Feature Recognition and Quality Prediction Models in Intelligent Production Line for Broiler Carcasses

Abstract

With global broiler production reaching 103 million tons in 2024—a 1.5% increase over 2023—the poultry industry continues to grow rapidly. However, traditional broiler segmentation methods struggle to meet modern demands for speed, precision, and adaptability. First, this study proposes an improved lightweight image segmentation algorithm based on YOLOv8-seg and integrates the Segment Anything Model (SAM) for semi-automatic annotation, achieving precise mask segmentation of broiler parts. Subsequently, Key geometric features (e.g., area, perimeter, axes) were extracted using image processing techniques, with enhancements from HSV color transformation, convex hull optimization, and ellipse fitting. Furthermore, Image calibration was applied to convert pixel data to physical dimensions, enabling real-sample validation. Using these features, multiple regression models—including CNNs—were developed for carcass quality prediction. Finally, by analyzing the broiler segmentation process, machine vision techniques were effectively integrated with quality grading algorithms and applied to intelligent broiler segmentation production lines, providing technical support for the intelligent and efficient processing of poultry products. The improved YOLOv8-seg model achieved mAP@0.5:box scores of 99.2% and 99.4%, and the CNN model achieved R² values of 0.974 (training) and 0.953 (validation). Compared to traditional systems, the intelligent broiler cutting line reduced failure rates by 11.38% and improved operational efficiency by over 3%, offering a reliable solution for automated poultry processing.

Keywords

Broiler carcass / Machine vision / YOLOv8-seg / Feature extraction / Quality grading / Intelligent production line

Author Contributions

Conceptualization, Z.H. and C.L.; Methodology, Y.H.; Software, M.C. and C.D.; Validation, J.G., Z.Y. and X.W.; Investigation, Z.H.; Data Curation, C.L.; Writing—Original Draft Preparation, Z.H.; Writing—Review & Editing, C.L.; Supervision, Y.Z., H.W., Y.S. and X.G.; Funding Acquisition, C.L.

Ethics Statement

Not applicable.

Informed Consent Statement

Not applicable.

Funding

This study was financially Supported by National Natural Science Foundation of China (52375447), the Special Fund of Taishan Scholars Project, the Shandong Province Youth Science and Technology Talent Support Project (Grant No.SDAST2024QTA043), and the Open Funding of Key Lab of Industrial Fluid Energy Conservation and Pollution Control, Ministry of Education (Grant Nos. CK-2024-0031, CK-2024-0035 and CK-2024-0036).

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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