Real Time Chicken Egg Size Classification Using Yolov4 Algorithm
DOI:
10.47709/brilliance.v4i2.4496Keywords:
Yolov4, Real time, Chicken EggDimension Badge Record
Abstract
The common problem currently faced by MSMEs producing chicken eggs is experiencing difficulties in grouping egg sizes every day. Currently, grouping egg sizes is still done manually, this is less than optimal and prone to errors so that many business owners often experience losses. Grouping egg sizes before being sold is very important to note because each size affects the selling price of eggs. The use of technology on a MSME scale in laying hen farmers has not been widely adopted, this is due to limited access and understanding of technology so that to improve and strengthen productivity, management, and marketing in this business, technological innovation is needed. One alternative solution to deal with this problem is to build a real-time computerized system that can group eggs according to their size. This study aims to evaluate the performance of the Yolov4 algorithm in grouping egg sizes based on their size in real time. Based on the results of the tests carried out, the Yolov4 algorithm is able to group chicken eggs in real time with an F1-Score value: 0.89 where the F1-Score value approaching 1 indicates that the system performance has been running well. The results of this classification can be used to create a real-time egg size grouping application that can help MSMEs to monitor the productivity of chicken eggs every day.
Abstract viewed = 17 times
References
Arulalan, V., Javali, T., Jha, S., & Garg, P. (2022). Object Detection Using YOLOv4 and PSO-CNN. International Research Journal of Modernization in Engineering Technology and Science, 04(06), 273–282. https://doi.org/10.1007/978-3-031-69197-3_22
Chary, P. S. (2023). Real Time Object Detection Using YOLOv4. International Journal for Research in Applied Science and Engineering Technology, 11(12), 1375–1379. https://doi.org/10.22214/ijraset.2023.57602
Geethanjali, T. M. (2023). Real Time Object Detection & Recognition?: A Comparative Study of YOLOv3 and YOLOv7 in OpenCV. Journal of Propulsion Technology, 44(5), 1903–1914.
Haq, M. A., Huy, L. N. Q., & Fahriani, N. (2024). Improving YOLO Object Detection Performance on Single-Board Computer using Virtual Machine. Emerging Information Science and Technology, 5(1), 36–45. https://doi.org/10.18196/eist.v5i1.22486
Imanuel, F., Waruwu, S. K., Linardy, A., & Husein, A. M. (2024). Journal of Computer Networks , Architecture and High Performance Computing Literature Review Application of YOLO Algorithm for Detection and Tracking Journal of Computer Networks , Architecture and High Performance Computing. Journal of Computer Networks, Architecture and High Performance Computing Volume, 6(3), 1378–1383.
Ishak, F., Restu Wardhana, I. A., Mutiara, G. A., Periyadi, Meisaroh, L., & Alfarisi, M. R. (2024). Improving the Productivity of Laying Hens Through a Modern Cage Cleanliness Monitoring System that Utilizes Integrated Sensors and IoT Technology. Journal of Robotics and Control (JRC), 5(4), 992–1001. https://doi.org/10.18196/jrc.v5i4.21610
Joe, S. Y., So, J. H., Oh, S. E., Jun, S., & Lee, S. H. (2024). Discharge Phenomenon. Foods Article, 13(13), 1–15.
Kelik Nugroho, A., Permadi, I., & Habiballah, A. (2023). Image Detection in the Aimbot Program Using Yolov4-Tiny. Jurnal Teknik Informatika (Jutif), 4(1), 109–115. https://doi.org/10.52436/1.jutif.2023.4.1.821
Lavanya, G., & Pande, S. D. (2024). Enhancing Real-time Object Detection with YOLO Algorithm. EAI Endorsed Transactions on Internet of Things, 10, 1–9. https://doi.org/10.4108/eetiot.4541
Li, L., Fang, B., & Zhu, J. (2022). Performance Analysis of the YOLOv4 Algorithm for Pavement Damage Image Detection with Different Embedding Positions of CBAM Modules. Applied Sciences (Switzerland), 12(19). https://doi.org/10.3390/app121910180
Maleh, I. M. D., Teguh, R., Sahay, A. S., Okta, S., & Pratama, M. P. (2023). Implementasi Algoritma You Only Look Once (YOLO) Untuk Object Detection Sarang Orang Utan Di Taman Nasional Sebangau. Jurnal Informatika, 10(1), 19–27. https://doi.org/10.31294/inf.v10i1.13922
Muwardi, R., Faizin, A., Adi, D. P., Rahmatullah, R., Wang, Y., Yunita, M., & Mahabror, D. (2023). Design Human Object Detection Yolov4-Tiny Algorithm on ARM Cortex-A72 and A53. Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika (JITEKI), 9(4), 1168–1178. https://doi.org/10.26555/jiteki.v9i4.27402
Redmon, J., & Farhad, A. (2018). YOLOv3: An Incremental Improvement. ArXiv Preprint ArXiv.
Rizal, R. A., Purba, N. O., Siregar, L. A., Sinaga, K., & Azizah, N. (2020). Analysis of Tuberculosis (TB) on X-ray Image Using SURF Feature Extraction and the K-Nearest Neighbor (KNN) Classification Method. Jaict, 5(2), 9–12. https://jurnal.polines.ac.id/index.php/jaict/article/view/1979
Ruhnke, I., Boshoff, J., Cristiani, I. V., Schneider, D., Welch, M., Sibanda, T. Z., & Kolakshyapati, M. (2019). Free-range laying hens: Using technology to show the dynamics and impact of hen movement. Animal Production Science, 59(11), 2046–2056. https://doi.org/10.1071/AN19256
Sandy, C. L. M., Husna, A., Rizal, R. A., & Muhathir, M. (2023). REAL TIME DETECTION OF CHICKEN EGG QUANTITY USING GLCM AND SVM CLASSIFICATION METHOD. Jurnal Techno Nusa Mandiri, 20(2), 108–114. https://doi.org/10.33480/techno.v20i2.4735
van Veen, L. A., van den Oever, A. C. M., Kemp, B., & van den Brand, H. (2023). Perception of laying hen farmers, poultry veterinarians, and poultry experts regarding sensor-based continuous monitoring of laying hen health and welfare. Poultry Science, 102(5), 102581. https://doi.org/10.1016/j.psj.2023.102581
Wang, C., & Liao, H. M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. ArXiv Preprint ArXiv.
Downloads
ARTICLE Published HISTORY
Accepted Date: 2024-08-10
Published Date: 2024-11-01
How to Cite
Issue
Section
License
Copyright (c) 2024 Cut Lika Mestika Sandy, Asmaul Husna, Reyhan Achmad Rizal
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
info@itscience.org, web: 
