Proceedings of International Conference on Applied Innovation in IT
2025/08/29, Volume 13, Issue 4, pp.153-158
Eggs Detection and Classification Using YOLOv5
Wesam Basil Abdulhameed, Zaidoon Tareq Abdulwahab, Mahmood Maher Salih and Mohammed Aktham Ahmed Abstract: As poultry product quality increases, so does the need for effective poultry egg sorting due to the limitations of manual processes. Modern farms now utilize automated systems to enhance productivity and accuracy, as well as improve animal welfare. An intelligent egg detection and classification system based on deep learning is proposed in this study. A dataset consisting of white and brown chicken eggs was collected and annotated alongside multiple variants of YOLOv5 to train and evaluate them. Various metrics including precision, recall, F1 score, mAP, and computational time were measured to determine how effective each model was. Results showed that the YOLOv5n model outperformed the rest with an F1 score of 0.98, along with achieving excellent detection accuracy and low computational requirements, thus showing suitability for real time applications. The work done in this paper demonstrated the possibilities given by computer vision in automating egg sorting and laid the groundwork for applying such systems into fully autonomous poultry farms.
Keywords: Object Detection, Deep Learning, Poultry, Eggs Collecting, YOLO.
DOI: 10.25673/122080
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References:
- M. Sinclair, Y. Zhang, K. Descovich, and C. J. Phillips, “Farm animal welfare science in China - A bibliometric review of Chinese literature,” Animals, vol. 10, no. 3, p. 540, 2020.
- M. A. Abed, Z. Al-Qaysi, and M. Suzani, “Improving lumbar disc bulging detection in MRI spinal imaging: A deep learning approach,” Al-Salam Journal for Engineering and Technology, vol. 4, no. 1, pp. 1–19, 2025.
- L. Cui, W. Tang, X. Deng, and B. Jiang, “Farm animal welfare is a field of interest in China: A bibliometric analysis based on CiteSpace,” Animals, vol. 13, no. 19, p. 3143, 2023.
- P. H. Hemsworth, “Cage production and laying hen welfare,” Animal Production Science, vol. 61, no. 10, pp. 821–836, 2021.
- M. H. Abdullah, M. Ahmed, Z. Al-Qaysi, and S. Abdulateef, “Feature-driven human action recognition from mobile sensor data using deep learning networks,” in Proc. 8th Int. Symp. Multidisciplinary Studies and Innovative Technologies (ISMSIT), 2024, pp. 1–7.
- D. Berckmans, “General introduction to precision livestock farming,” Animal Frontiers, vol. 7, no. 1, pp. 6–11, 2017.
- W. Yang et al., “Deformable convolution and coordinate attention for fast cattle detection,” Computers and Electronics in Agriculture, vol. 211, p. 108006, 2023.
- C. Fang, Z. Wu, H. Zheng, J. Yang, C. Ma, and T. Zhang, “MCP: Multi-chicken pose estimation based on transfer learning,” Animals, vol. 14, no. 12, p. 1774, 2024.
- M. A. Ahmed et al., “Taxonomy, open challenges, motivations, and recommendations in driver behavior recognition: A systematic review,” Iraqi Journal for Computer Science and Mathematics, vol. 5, no. 3, p. 17, 2024.
- E. D. Ellen et al., “Review of sensor technologies in animal breeding: Phenotyping behaviors of laying hens to select against feather pecking,” Animals, vol. 9, no. 3, p. 108, 2019.
- D. Wu, D. Cui, M. Zhou, and Y. Ying, “Information perception in modern poultry farming: A review,” Computers and Electronics in Agriculture, vol. 199, p. 107131, 2022.
- L. Candelotto, K. J. Grethen, C. M. Montalcini, M. J. Toscano, and Y. Gómez, “Tracking performance in poultry is affected by data cleaning method and housing system,” Applied Animal Behaviour Science, vol. 249, p. 105597, 2022.
- J. Yang, T. Zhang, C. Fang, H. Zheng, C. Ma, and Z. Wu, “A detection method for dead caged hens based on improved YOLOv7,” Computers and Electronics in Agriculture, vol. 226, p. 109388, 2024.
- T. Norton, C. Chen, M. L. V. Larsen, and D. Berckmans, “Precision livestock farming: Building digital representations to bring the animals closer to the farmer,” Animal, vol. 13, no. 12, pp. 3009–3017, 2019.
- C. Fang, T. Zhang, H. Zheng, J. Huang, and K. Cuan, “Pose estimation and behavior classification of broiler chickens based on deep neural networks,” Computers and Electronics in Agriculture, vol. 180, p. 105863, 2021.
- Z. Wu et al., “Super-resolution fusion optimization for poultry detection: A multi-object chicken detection method,” Journal of Animal Science, vol. 101, p. skad249, 2023.
- Z. Al-Qaysi et al., “Generalized time domain prediction model for motor imagery-based wheelchair movement control,” Mesopotamian Journal of Big Data, pp. 68–81, 2024.
- A. F. Ab Nasir, S. S. Sabarudin, A. P. A. Majeed, and A. S. A. Ghani, “Automated egg grading system using computer vision: Investigation on weight measure versus shape parameters,” in IOP Conf. Series: Materials Science and Engineering, vol. 342, no. 1, p. 012003, 2018.
- J. Alikhanov et al., “Design and performance of an automatic egg sorting system based on computer vision,” TEM Journal, vol. 8, no. 4, p. 1319, 2019.
- C. Okinda et al., “Egg volume estimation based on image processing and computer vision,” Journal of Food Engineering, vol. 283, p. 110041, 2020.
- S. R. Dibakoane et al., “The application of multi-elemental fingerprints and chemometrics for discriminating between cage and free-range table eggs,” Journal of Food Measurement and Characterization, vol. 17, no. 4, pp. 3802–3808, 2023.
- M. Turkoglu, “Defective egg detection based on deep features and bidirectional long short-term memory,” Computers and Electronics in Agriculture, vol. 185, p. 106152, 2021.
- X. Wang, X. Yue, H. Li, and L. Meng, “A high-efficiency dirty-egg detection system based on YOLOv4 and TensorRT,” in Proc. Int. Conf. Advanced Mechatronic Systems (ICAMechS), 2021, pp. 75–80.
- T. Jiang et al., “Improved YOLOv8 model for lightweight pigeon egg detection,” Animals, vol. 14, no. 8, p. 1226, 2024.
- T. Wang, S. Ren, and H. Zhang, “Nighttime wildlife object detection based on YOLOv8-night,” Electronics Letters, vol. 60, no. 15, p. e13305, 2024.
- K. Jiang et al., “An attention mechanism-improved YOLOv7 object detection algorithm for hemp duck count estimation,” Agriculture, vol. 12, no. 10, p. 1659, 2022.
- L. Yu et al., “A lightweight neural network-based method for detecting estrus behavior in ewes,” Agriculture, vol. 12, no. 8, p. 1207, 2022.
- M. A. Habeeb, Y. L. Khaleel, R. D. Ismail, Z. Al-Qaysi, and F. N. Ameen, “Deep learning approaches for gender classification from facial images,” Mesopotamian Journal of Big Data, pp. 185–198, 2024.
- M. Hadid, Q. M. Hussein, Z. Al-Qaysi, M. Ahmed, and M. M. Salih, “An overview of content-based image retrieval methods and techniques,” Iraqi Journal for Computer Science and Mathematics, vol. 4, no. 3, p. 6, 2023.
- O. N. Haggab and Z. Al-Qaysi, “Detecting defect in central pivot irrigation system using YOLOv7 algorithms,” Al-Salam Journal for Engineering and Technology, vol. 3, no. 2, pp. 38–49, 2024.
- R. P. Neelakandan et al., “Early detection of Alzheimer’s disease: An extensive review of advancements in machine learning mechanisms using ensemble and deep learning techniques,” Engineering Proceedings, vol. 59, no. 1, p. 10, 2023.
- S. K. Abdulateef, “Evolutionary optimization of geometrical image contour detection,” International Journal of Intelligent Engineering & Systems, vol. 15, no. 2, 2022.
- A. K. Mustfa et al., “Classification flower images based on deep learning and machine learning,” in Proc. 8th Int. Symp. Multidisciplinary Studies and Innovative Technologies (ISMSIT), 2024, pp. 1–5.
- L. R. Sultan, S. K. Abdulateef, and B. Shtayt, “Prediction of student satisfaction on mobile learning by using fast learning network,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 27, no. 1, pp. 488–495, 2022.
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