The Use of a Vision System for Monitoring Chick Embryos in Incubator


  • Abdullah Al-Ansi Thamar University
  • V Ryabtsev European University
  • T Utkina Cherkassy State Technological University



Image Analysis, Incubation, Chicken Embryos, Candling, Technical Sap, Piece neuron mesh


Background: Determine the quality of hatching eggs and control the process of their embryonic development, it is necessary to perform candling of eggs, which makes it possible to identify and eliminate the causes of low hatching of chicks.

Contribution: The proposed vision system for processing images of chicken eggs makes it possible to distinguish fertilized eggs from unfertilized ones, which makes it possible to change the parameters of the incubator in the event of a large number of dead embryos.

Method: With the help of the National Instruments Vision Assistant module, which simplifies the collection, reproduction and storage of images recorded using a video camera connected via a FireWire interface, an incubator vision system has been developed.

Results: The yield of healthy chicks is increased due to the periodic automatic candling of eggs and the timely regulation of parameters during the incubation process.

Conclusion: Being able to monitor embryo development more closely allows you to determine the optimal time to change incubation parameters, such as humidity, to create the best conditions for egg hatching, leading to more efficient chick production. Similarly, the ability to use different light and temperature conditions to influence chick hatch time can allow for a faster incubation process and hatch healthy chicks in a shorter period of time.

Author Biographies

Abdullah Al-Ansi, Thamar University

Faculty of Education

V Ryabtsev , European University

Cherkassy branch of the European University


M. Hashemzadeh, N. Farajzadeh, “A Machine Vision System for Detecting Fertile Eggs in the Incubation Industry,” International Journal of Computational Intelligence Systems, vol. 9, no. 5, pp. 850-862, 2016,

F. G. M. Fasenko, “Egg Storage and the Embryo,” Poultry Science, vol. 86, no. 5 pp. 1020-1024, 2007,

M. Boğa, K.K. Çevik, H.E. Koçer, A. Burgut, “Computer-assisted automatic egg fertility control,” Kafkas Universitesi Veteriner Fakultesi Dergisi, vol. 25, no. 4, pp. 567-574, 2019,

M. H. Islam, N. Kondo, Y. Ogawa, T. Fujiura, Y. Ogawa, S. Fujitani, “Detection of Infertile Eggs using Visible Transmission Spectroscopy Combined with Multivariate Analysis,” Engineering in Agriculture, Environment and Food, vol. 10, no. 2, pp. 115-120, 2017,

T. Georgieva, E. Stefanov, J. Alikhanov, Z. Shynybay, A. Kulmakhambetova, P. Daskalov, “Approach for Egg Defects Assessment Using Image Analysis,” Proceedings of the 30th DAAAM International Symposium on Intelligent Manufacturing and Automation, pp. 1102-1106, 2019,

J. Alikhanov et al., “Design and Performance of an Automatic Egg Sorting System Based on Computer Vision,” UIKTEN - Association for Information Communication Technology Education and Science, vol. 8, no. 4, pp. 1319-1325, 2019,

M. Boğa, K.K. Çevik, H.E. Koçer, A. Burgut, “Computer-assisted automatic egg fertility control,” Kafkas Universitesi Veteriner Fakultesi Dergisi, vol. 25, no. 4, pp. 567-574, 2019,

Al-Ansi, Abdullah M., Mudar Almadi, Parul Ichhpujani, and Vladimir Ryabtsev. "Eidos System Prediction of Myopia in Children in Early Education Stages." Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) 9, no. 2 (2023): 411-419.

A. J. Ewald, H. McBride, M. Reddington, S. E. Fraser, R. Kerschmann, “Surface Imaging Microscopy, an Automated Method for Visualizing Whole Embryo Samples in Three Dimensions at High Resolution,” Developmental Dynamics, vol. 225, no. 3, pp. 369-75, 2002,

A. M. Al-Ansi, A. Al-Ansi, "An Overview of Artificial Intelligence (AI) in 6G: Types, Advantages, Challenges and Recent Applications," Buletin Ilmiah Sarjana Teknik Elektro, vol. 5, no. 1, pp. 67-75, 2023,

K. Das, M. D. Evans, “Detecting Fertility of Hatching Eggs using Machine Vision II: Neural Network Classifiers,” American Society of Agricultural and Biological Engineers, vol. 35, no. 6, pp. 2035-2041, 1992,

B.G. Kukharenko, Algorithms for analyzing the components of hyperspectral images, Information technologies. No. 6, - pp. 1-32. 2013.

S. Subedi, R. Bist, X. Yang, L. Chai, “Tracking floor eggs with machine vision in cage-free hen houses,” Poultry Science, vol. 102, no. 6, p. 102637, 2023,

A. J. Ewald, H. McBride, M. Reddington, S. E. Fraser, R. Kerschmann, “Surface Imaging Microscopy, an Automated Method for Visualizing Whole Embryo Samples in Three Dimensions at High Resolution,” Developmental Dynamics, vol. 225, no. 3, pp. 369-75, 2002,

S. Yu, S. Jia, C. Xu, “Convolutional Neural Networks for Hyperspectral Image Classification,” Neurocomputing, vol. 219, pp. 88-98, 2017,

T.Yu. Utkina, V.G. Ryabtsev, “Monitoring the development of chicken eggs based on the neural network of embryo state recognition,” Bulletin of the Khmelnytskyi National University, no. 6, pp. 95-101, 2021,

S. Khaikin Neural networks: a complete course. Ed. 2 nd, rev.; per. from English. Moscow: OOO I.D. Williams”, 2006. 1104

A. Nellutla, “Image Processing Techniques Using LabVIEW,” International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS), vol. 8, no. 8, 2018,

D. V. Pradun, B.A. Zalessky, Automatic binarization of gradient images based on the cluster method for determining the threshold value, Informatics, No. 1, 2010. - P. 5-12.

Yu. V. Vizilter, S. Yu. Zheltov, V. A. Knyaz, A. N.Khodarev, A. V., Morzhin Processing and analysis of digital images with examples on LabVIEW MAQ Vision. – M.: DMK Press, 2007. - 464 p.

V. Makhov, V. Shirobokov, A. Zakutaev, Building vision systems based on computer technologies National Instruments, Control Engineering, No. 4 (76), 2018. - Pp. 62-69.

I.V. Groshev, V.I. Korolkov, Vision systems and image processing. Tutorial. M.: RUDN, 2008. - 212 p.

V.P. Fedosov, A.K. Nesterenko, Digital signal processing in LabVIEW: textbook. Allowance ed. in. P. Fedosov. – M.: DMK Press, 2007. – 456 p.

M. E. Paoletti, J. M. Haut, J. Plaza, A. Plaza, “Deep Learning Classifiers for Hyperspectral Imaging: A Review,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 158, pp. 279-317, 2019,

A. M. Al-Ansi, M. Jaboob, A. M. S. B. Awain, “Examining the Mediating Role of Job Satisfaction between Motivation, Organizational Culture, and Employee Performance in Higher Education: A Case Study in the Arab Region,” Education Science and Management, vol. 1, no. 1, pp. 30-42, 2023,

A. M. Al-Ansi, M. Jaboob, A. Garad, A. Al-Ansi, “Analyzing Augmented Reality (AR) and Virtual Reality (VR) Recent Development in Education,” Social Sciences & Humanities Open, vol. 8, no. 1, p. 100532, 2023,

E. B. Junior, A. Setti, D. Braga, R. Provenza, P. Guilherme, A. Iaconelli, “O-208 Use of epididymal spermatozoa in in vitro fertilization cycles impacts the morphokinetics of embryos cultured in a time-lapse imaging incubator system,” Human Reproduction, vol. 37, no. 1, 2022,

K. Tan, A. R. Kavsaoğlu, O. Koçak and C. Akbay, "Remote Monitoring System For Incubator Data," 2018 Medical Technologies National Congress (TIPTEKNO), pp. 1-4, 2018,

I. Santoso, G. R. Wilis, M. A. Shidik, “U Shield 3 Axis CNC Router Training in Tegal City Metal Group to Improve Machinery Capability,” SPEKTA (Jurnal Pengabdian Kepada Masyarakat: Teknologi Dan Aplikasi), vol. 3, no. 2, pp. 175–866, 2022,

S. Kermanshahani, H. R. Hamidi, “Flexible Data Refreshing Architecture for Health Information System Integration,” SPEKTA (Jurnal Pengabdian Kepada Masyarakat: Teknologi Dan Aplikasi), vol. 4, no. 1, 2023,

A. M. Al-Ansi, “Reinforcement of Student-Centered Learning through Social e-Learning and e-Assessment,” SN Social Sciences, vol. 2, no. 9, 2022,

A. M. Al-Ansi, A. Al-Ansi, “Enhancing Student-Centered Learning through Introducing Module for STEM Development and Assessment,” International Journal of STEM Education for Sustainability, vol. 3, no. 1, pp. 22–27, 2023,

A. Khaliduzzaman et al., “Non-Invasive Characterization of Chick Embryo Body and Cardiac Movements Using near Infrared Light,” Engineering in Agriculture, Environment and Food, vol. 12, no. 1, pp. 32–39, 2019,

A. Khaliduzzaman, S. Fujitani, A. Kashimori, T. Suzuki, Y. Ogawa, N. Kondo, “A Non-Invasive Diagnosis Technique of Chick Embryonic Cardiac Arrhythmia Using near Infrared Light,” Computers and Electronics in Agriculture, vol. 158, pp. 326–34, 2019,

S. Meyhöfer et al., “Evaluation of a near Infrared Light Ultrasound System as a Non‐invasive Blood Glucose Monitoring Device,” Pubmed, vol. 22, no. 4, pp. 694-698, 2019,

A. Phuphanin, L. Sampanporn, B. Sutapun, “Smartphone-Based Device for Non-Invasive Heart-Rate Measurement of Chicken Embryos,” Sensors, vol. 19, no. 22, p. 4843, 2019,

O. Görgülü and A. Akilli, “Egg Production Curve Fitting Using Least Square Support Vector Machines and Nonlinear Regression Analysis,” European Poultry Science (EPS), vol. 82, 2018,

A. Garad, G. Budiyanto, A. M. Al-Ansi, "Impact of covid-19 pandemic on the global economy and future prospects: A systematic review of global reports," Journal of Theoretical and Applied Information Technology, vol. 99, no. 4, pp. 1-15, 2021,

J. Xiong, S. Tian, C. Yang, “Fault Modeling on Complex Field Using Least-Square Circle Fitting for Linear Analog Circuits,” IEEJ Transactions on Electrical and Electronic Engineering, vol. 12, no. 5, pp. 638–45, 2017,




How to Cite

Al-Ansi, A., Ryabtsev , V., & Utkina , T. (2023). The Use of a Vision System for Monitoring Chick Embryos in Incubator. SPEKTA (Jurnal Pengabdian Kepada Masyarakat : Teknologi Dan Aplikasi), 4(2), 266–275.



Applied Technology