IoT-Based Egg Incubator with MobileNetV2 CNN for Candling Image Classification and Fertility Detection
DOI:
https://doi.org/10.35194/mji.v18i1.6368Keywords:
Automatic Egg Incubator , Candling , ESP32-CAM , Internet of Things , MobileNetV2Abstract
Egg incubation and fertility detection are critical processes in poultry production, requiring stable environmental conditions and accurate embryo monitoring. Conventional incubation and candling methods often rely on manual observation, which may reduce efficiency and increase the risk of human error. This study aims to develop an IoT-based automatic egg incubator integrated with a MobileNetV2-based Convolutional Neural Network (CNN) for candling image classification and fertility detection. The proposed system combines an ESP32-based incubation monitoring platform, ESP32-CAM image acquisition module, ESP-NOW wireless communication, and a web-based monitoring interface. Candling images were collected and classified into three categories: live fertile, dead fertile, and infertile eggs. Transfer learning using the MobileNetV2 architecture was employed to train the classification model. The developed CNN model achieved an accuracy of 91.26% on the testing dataset and 82.22% during validation under real-world conditions. Performance evaluation showed precision values up to 0.965, recall values up to 0.967, and F1-scores above 0.89 across the three classes. Furthermore, the integrated system successfully enabled real-time monitoring of incubation conditions, automated image acquisition, and web-based fertility classification. System testing using 45 egg samples demonstrated that the proposed solution could effectively identify embryo development and fertility status. The integration of IoT technology, ESP32-CAM-based candling, and MobileNetV2 CNN provides an effective solution for automated incubation monitoring and egg fertility detection. The developed system improves monitoring efficiency, reduces manual intervention, and supports decision-making during the incubation process.
References
[1] K. K. Çevik, H. E. Koçer, and M. Boğa, “Deep learning based egg fertility detection,” Veterinary Sciences, vol. 9, no. 10, Art. no. 574, 2022, doi: 10.3390/vetsci9100574.
[2] A. Dhillon and G. K. Verma, “Convolutional neural network: A review of models, methodologies and applications to object detection,” Progress in Artificial Intelligence, vol. 9, no. 2, pp. 85–112, 2020, doi: 10.1007/s13748-019-00203-0.
[3] L. K. S. Tolentino, E. J. G. Enrico, R. L. M. Listanco, M. A. M. Ramirez, T. L. U. Renon, and M. R. B. Samson, “Development of fertile egg detection and incubation system using image processing and automatic candling,” in Proc. TENCON 2018—2018 IEEE Region 10 Conference, 2018, pp. 701–706, doi: 10.1109/TENCON.2018.8650320.
[4] D. W. Garcia and G. Magwili, “An automated candling system for duck egg fertility detection, sorting, and counting via digital image processing,” in Proc. 2021 11th International Workshop on Computer Science and Engineering (WCSE), 2021, pp. 93–100, doi: 10.18178/wcse.2021.06.014.
[5] M. I. S. T. Ariffin, N. I. A. N. Amin, M. A. H. A. Abas, R. L. Jamil, and S. Z. Yusof, “Designing a chicken egg incubator with IoT-based control,” Asian Journal of Vocational Education and Humanities, vol. 6, no. 1, pp. 28–33, 2025, doi: 10.53797/ajvah.v6i1.4.2025.
[6] M. C. A. Prabowo, I. Sayekti, S. Astuti, S. T. Nursaputro, and Supriyati, “Development of an IoT-based egg incubator with PID control system and mobile application,” International Journal on Informatics Visualization, vol. 8, no. 1, pp. 465–472, 2024.
[7] S. Saifullah, R. Drezewski, A. Yudhana, A. Pranolo, W. Kaswijanti, A. P. Suryotomo, S. A. Putra, A. Khaliduzzaman, A. S. Prabuwono, and N. Japkowicz, “Nondestructive chicken egg fertility detection using CNN-transfer learning algorithms,” Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, vol. 9, no. 3, pp. 854–871, 2023, doi: 10.26555/jiteki.v9i3.26722.
[8] M. Hashemzadeh and 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.
[9] L. Geng, Y. Hu, Z. Xiao, and J. Xi, “Fertility detection of hatching eggs based on a convolutional neural network,” Applied Sciences, vol. 9, no. 7, Art. no. 1408, 2019, doi: 10.3390/app9071408.
[10] B. Praveena, K. Punith, K. Kumar, S. Rabbani, and P. Geetha, “Implementation of smart agriculture using ESP32-based IoT and machine learning for monitoring crop,” in Proc. ICTMIM, 2026, pp. 399–404, doi: 10.1109/ICTMIM68190.2026.11507902.
[11] M. M. Taye, “Theoretical understanding of convolutional neural network: Concepts, architectures, applications, future directions,” Computation, vol. 11, no. 3, Art. no. 52, 2023, doi: 10.3390/computation11030052.
[12] R. Aisuwarya, R. Ferdian, and S. Wafiq, “Deep learning-based egg fertility classification with Raspberry Pi and color sensor integration,” in Proc. 2025 International Seminar on Intelligent Technology and Its Applications (ISITIA), 2025, pp. 584–589, doi: 10.1109/ISITIA66279.2025.11137566.
[13] J. E. L. Ramos, B. T. Sanchez, J. T. Bojocan, and E. A. Tacda, “Real-time tray-level egg fertility detection on Raspberry Pi using YOLOv5 and custom LED backlighting,” in Proc. 2025 9th International Artificial Intelligence and Data Processing Symposium (IDAP), 2025, pp. 1–7, doi: 10.1109/IDAP68205.2025.11222310.
[14] D. Musara, B. Sarema, D. Mashava, K. Chinguwo, and T. M. Muhla, “Design of an AI-based egg fertility detection system for incubators,” Open Access Research Journal of Science and Technology, vol. 12, no. 1, pp. 1–9, 2024, doi: 10.53022/oarjst.2024.12.1.0109.
[15] J. Astill, R. A. Dara, E. D. G. Fraser, B. Roberts, and S. Sharif, “Smart poultry management: Smart sensors, big data, and the internet of things,” Computers and Electronics in Agriculture, vol. 170, Art. no. 105291, 2020, doi: 10.1016/j.compag.2020.105291.
[16] S. Saifullah and R. Dreżewski, “Non-destructive egg fertility detection in incubation using SVM classifier based on GLCM parameters,” Procedia Computer Science, vol. 207, pp. 3254–3263, 2022, doi: 10.1016/j.procs.2022.09.383.
[17] K. Muttaqin, A. Ihsan, and H. Irawan, “Peningkatan produktivitas ternak ayam melalui teknologi inkubator mesin penetas telur berbasis Internet of Thing,” JMM (Jurnal Masyarakat Mandiri), vol. 6, no. 5, pp. 4395–4408, 2022, doi: 10.31764/jmm.v6i5.10812.
[18] M. W. Ahmed, A. Sprigler, J. L. Emmert, R. N. Dilger, G. Chowdhary, and M. Kamruzzaman, “Non-destructive detection of pre-incubated chicken egg fertility using hyperspectral imaging and machine learning,” Smart Agricultural Technology, vol. 10, Art. no. 100857, 2025, doi: 10.1016/j.atech.2025.100857.
[19] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “MobileNetV2: Inverted residuals and linear bottlenecks,” in Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 4510–4520.
[20] A. O. Adegbenjo, L. Liu, and M. O. Ngadi, “Non-destructive assessment of chicken egg fertility,” Sensors, vol. 20, no. 19, Art. no. 5546, 2020, doi: 10.3390/s20195546.
[21] S. P. Santosa, J. Jaylana, and N. Naibaho, “Smart egg incubator monitoring system based on IoT and Blynk connectivity,” JTEIN: Jurnal Teknik Elektro Indonesia, vol. 6, no. 2, pp. 172–181, 2025.
[22] M. F. Amrulloh and M. Syarwani, “Sistem monitoring suhu pada kandang ayam menggunakan ESP8266 dan sensor DHT11 berbasis IoT,” Neutral: Journal of Engineering, vol. 1, no. 1, pp. 9–13, 2023.
[23] U. W. Yuda and T. Sutabri, “Pengembangan inkubator telur ayam berbasis IoT dan Arduino dengan metode prototipe sistem kontrol suhu,” Jurnal Sains Student Research, vol. 3, no. 2, pp. 401–409, 2025, doi: 10.61722/jssr.v3i2.4321.
[24] N. M. Nizarudeen, R. Sridhar, J. Abdul Rahman, and A. Sugumar, “AI-enabled smart egg incubator using ESP32-CAM for automated environmental control and fertility detection,” International Journal of Research and Innovation in Applied Science, vol. 10, no. 11, pp. 1510–1521, 2025, doi: 10.51584/IJRIAS.2025.101100138.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Irfan Rifqy Widya Syahbani, Fadila Azahra, Della Arviyanti, Christiano Nicoma Boseke, Ariel Pasha Ramaditya, Adinda Octhavia Indriyani, Fathir Gunadireja, Setya Mega Bagaskara, Muhammad Nasir, Gema Parasti Mindara
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
