| _version_ | 1866901888419495936 |
|---|---|
| author | Batanov S Baranova I Yedige N Serimbek A Darkhan S Kazybay B |
| author_facet | Batanov S Baranova I Yedige N Serimbek A Darkhan S Kazybay B |
| contents | <p>This study focuses on the development and application of an automated system for conformation assessment of cattle using computer-based technologies, including neural network algorithms and 3D modeling. The aim of this study was to develop a modern method for evaluating animal body conformation using advanced digital technologies, as well as to analyze their exterior and determine the relationship between phenotypic features and genetic potential. The study subjects were Holstein cows bred in Kazakhstan. The methodology included the use of Intel RealSense D456 stereoscopic depth camera, Raspberry Pi 5, and the development of neural models for automatic detection of animal body measurements. Based on the image processing results, the accuracy was 96%, with an average deviation of 7.2 mm. The technology enables automated conformation assessment and provides fast and precise measurement of parameters. The results can be integrated with genomic data to improve selection decisions.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18348586 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Exterior Analysis and Conformation Assessment of Cattle Using Neural Networks and Computer Vision Technology Batanov S Baranova I Yedige N Serimbek A Darkhan S Kazybay B <p>This study focuses on the development and application of an automated system for conformation assessment of cattle using computer-based technologies, including neural network algorithms and 3D modeling. The aim of this study was to develop a modern method for evaluating animal body conformation using advanced digital technologies, as well as to analyze their exterior and determine the relationship between phenotypic features and genetic potential. The study subjects were Holstein cows bred in Kazakhstan. The methodology included the use of Intel RealSense D456 stereoscopic depth camera, Raspberry Pi 5, and the development of neural models for automatic detection of animal body measurements. Based on the image processing results, the accuracy was 96%, with an average deviation of 7.2 mm. The technology enables automated conformation assessment and provides fast and precise measurement of parameters. The results can be integrated with genomic data to improve selection decisions.</p> |
| title | Exterior Analysis and Conformation Assessment of Cattle Using Neural Networks and Computer Vision Technology |
| url | https://doi.org/10.5281/zenodo.18348586 |