. Currently, there is a high demand for poultry products. Breeding of poultry on an industrial scale is possible through the use of artificial incubators. An important factor for egg hatchability is a preliminary assessment of their suitability for incubation, and the use of computer vision systems (CVS) will improve the accuracy of this assessment and eliminate the error associated with the so-called "human factor" from the evaluation process, resulting in an increase in their hatchability. The method of assessing the suitability of eggs for incubation by CVS is not only accurate, but also non-invasive. This article describes a CVS for evaluating the quality of chicken eggs. A selection of hardware components and software for the development of the CVS has been made. A control program has been written to implement the interaction of the components of the CVS within its functionality. The functionality of the CVS includes image acquisition and processing, the determination of large and small egg diameters, and the output of information in text form. Within the framework of this work, the operation of the CVS at various positions of the analyzed samples is analyzed. A comparison of large and small diameters of eggs measured mechanically and by means of CVS has been carried out. The average measurement accuracy was 99.24%.
quality assessment, computer vision, egg classification, microcomputer, display, egg contour, detection, pixels, measurements
1. A computer-vision aided Compton-imaging system for radioactive waste characterization and decommissioning of nuclear power plants / V. Babiano-Suárez, J. Balibrea-Correa, I. Ladarescu [et al.] // Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 2025. V. 1076.https://doi.org/10.1016/j.nima.2025.170449 (data obrascheniya: 23.05.2025). DOI:https://doi.org/10.1016/j.nima.2025.170449( EDN: https://elibrary.ru/PFJEVN
2. Sustainable resource management in construction: Computer vision for recognition of electro-construction waste (ECW) / A. Senanayake, B. Gautam, Mehrtash Harandi [et al.] // Resources, Conservation and Recycling. 2025. V. 211.https://doi.org/10.1016/j.resconrec.2025.108380 (data obrascheniya: 23.05.2025). DOI:https://doi.org/10.1016/j.resconrec.2025.108380(
3. Senanayake A., Arashpour M. Automated Electro-construction waste Sorting: Computer vision for part-level segmentation // Waste Management. - 2025. V. 203.https://doi.org/10.1016/j.wasman.2025.114883 (data obrascheniya: 20.04.2025). DOI:https://doi.org/10.1016/j.wasman.2025.114883( EDN: https://elibrary.ru/QFRFCA
4. Song X. Emotional recognition and feedback of students in English e-learning based on computer vision and face recognition algorithms // Entertainment Computing. 2021. V. 92.https://doi.org/10.1016/j.compeleceng.2021.107128 (data obrascheniya: 23.04.2025). DOI:https://doi.org/10.1016/j.compeleceng.2021.107128(
5. Computer vision-based prototype robotic picking cum grading system for fruits / M.H. Dairath, M.W. Akram, K.A. Mehmood [et al.] // Smart Agricultural Technology. 2023. V. 4. EDN: https://elibrary.ru/HKLGEM
6. Development of an optimally designed real-time automatic citrus fruit grading-sorting machine leveraging computer vision-based adaptive deep learning model / S.K. Chakraborty, A. Subeesh, K. Dubey [et al.] // Engineering Applications of Artificial Intelligence. 2023. V. 120. EDN: https://elibrary.ru/XJNZAG
7. Wójcik S., Ciepiela F., Jakubowska M.Computer vision analysis of sample colors versus quadruple-disk iridium-platinum voltammetric e-tongue for recognition of natural honey adulteration // Measurement. 2023. V. 209.
8. Minz P.S., Saini C.S.Comparison of computer vision system and colour spectrophotometer for colour measurement of mozzarella cheese // Applied Food Research. 2021. V. 1. № 2. EDN: https://elibrary.ru/RFNIPA
9. Colour assessment of milk and milk products using computer vision system and colorimeter / B. Milovanovic, V. Tomovic, I. Djekic [et al.] // International Dairy Journal. 2021. V. 120. EDN: https://elibrary.ru/AIUPTJ
10. Harnsoongnoen S., Jaroensuk N. The grades and freshness assessment of eggs based on density detection using machine vision and weighing sensor // Sci Rep. 2021. V. 11.https://doi.org/10.1038/s41598-021-96140-x (data obrascheniya: 23.04.2025). DOI:https://doi.org/10.1038/s41598-021-96140-x( EDN: https://elibrary.ru/SRQGKJ
11. A Computer Vision-Based Automatic System for Egg Grading and Defect Detection / X. Yang, R.B. Bist, S. Subedi [et al.] // Animals. 2023. V. 13. P. 23-54.https://doi.org/10.3390/ani13142354 (data obrascheniya: 23.05.2025). DOI:https://doi.org/10.3390/ani13142354( EDN: https://elibrary.ru/FULYEC
12. Non-destructive detection of pre-incubated chicken egg fertility using hyperspectral imaging and machine learning / A. Wadud, S. Asher, L.E. Jason [et al.] // Smart Agricultural Technology. 2025. V. 10.https://doi.org/10.1016/j.atech.2025.100857 (data obrascheniya: 03.03.2025). DOI:https://doi.org/10.1016/j.atech.2025.100857(
13. Shofan S., Rafał D. Non-Destructive Egg Fertility Detection in Incubation Using SVM Classifier Based on GLCM Parameters // Procedia Computer Science. 2022. V. 207. P. 3254-3263.https://doi.org/10.1016/j.procs.2022.09.383 (data obrascheniya: 03.05.2025). DOI:https://doi.org/10.1016/j.procs.2022.09.383( EDN: https://elibrary.ru/NTFETL
14. CVAE-DF: A hybrid deep learning framework for fertilization status detection of pre-incubation duck eggs based on VIS/NIR spectroscopy / D. Wang, Q. Wang, Z. Chen [et al.] // Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. 2024. V. 320.https://doi.org/10.1016/j.saa.2024.124569 (data obrascheniya: 23.04.2025). DOI:https://doi.org/10.1016/j.saa.2024.124569( EDN: https://elibrary.ru/SADYCO
15. Digital imaging assisted geometry of chicken eggs using Hügelschäffer's model / V.G. Narushin, M.N. Romanov, G. Lu [et al.] // Biosystems Engineering. 2020. V. 197. P. 45-55.https://doi.org/10.1016/j.biosystemseng.2020.06.008 (data obrascheniya: 13.04.2025). DOI:https://doi.org/10.1016/j.biosystemseng.2020.06.008( EDN: https://elibrary.ru/MOKAEO
16. Crack Detection on Duck Eggs Using CNN / R.G. Caballero, J.R. Gucor, R.D.P. Talledo [et al.] // Procedia Computer Science. 2025. V. 257. P. 631-636.https://doi.org/10.1016/j.procs.2025.03.081 (data obrascheniya: 23.05.2025). DOI:https://doi.org/10.1016/j.procs.2025.03.081( EDN: https://elibrary.ru/YSESPI
17. Eggshell biometrics for individual egg identification based on convolutional neural networks / Z. Chen, P. He, Y. He [et al.] // Poultry Science. 2023. V. 102. № 4.https://doi.org/10.1016/j.psj.2023.102540 (data obrascheniya: 20.04.2025). DOI:https://doi.org/10.1016/j.psj.2023.102540(
18. Nakaguchi V.M., Abeyrathna R.M.R.D., Ahamed T. Development of a new grading system for quail eggs using a deep learning-based machine vision system // Computers and Electronics in Agriculture. 2024. V. 226.https://doi.org/10.1016/j.compag. 2024.109433 (data obrascheniya: 23.05.2025). DOI:https://doi.org/10.1016/j.compag.2024.109433( EDN: https://elibrary.ru/XSDACM
19. Victor Massaki Nakaguchi, R.M. Rasika D. Abeyrathna, Tofael Ahamed, Development of a new grading system for quail eggs using a deep learning-based machine vision system.Computers and Electronics in Agriculture, Volume 226,2024,109433,ISSN 0168 1699.https://doi.org/10.1016/j.compag.2024.109433. (data obrascheniya: 10.05.2025). DOI:https://doi.org/10.1016/j.compag.2024.109433.(
20. Eggshell biometrics for individual egg identification based on convolutional neural networks / Chen Zhonghao, He Pengguang, He Yefan [et al.] Poultry Science, Volume 102, Issue 4,2023,102540,ISSN 0032-5791.https://doi.org/10.1016/j.psj.2023.102540 (data obrascheniya: 23.05.2025). DOI:https://doi.org/10.1016/j.psj.2023.102540(
21. Obzor metodov mashinnogo obucheniya i komp'yuternogo zreniya v tehnologii inkubacii yaic sel'skohozyaystvennyh ptic / G.S. Shirmanova, E.N. Neverov, E.V. Nikolaeva [i dr.] // Vestnik APK Verhnevolzh'ya. 2024. № 4 (68). S. 92-99. URL: https://www.yaragrovuz.ru/images/Vestnik_APK/vapk2024468.pdf (data obrascheniya: 20.04.2025). EDN: https://elibrary.ru/SRLWTK
