REAL TIME DETECTION OF CHICKEN EGG QUANTITY USING GLCM AND SVM CLASSIFICATION METHOD

  • Cut Lika Mestika Sandy (1*) Universitas Islam Kebangsaan Indonesia
  • Asmaul Husna (2) Universitas Islam Kebangsaan Indonesia
  • Reyhan Achmad Rizal (3) Universitas Prima Indonesia
  • Muhathir Muhathir (4) Universitas Medan Area

  • (*) Corresponding Author
Keywords: Automation, Chicken Egg, GLCM, SVM

Abstract

A common problem currently being faced in the chicken egg production home industry is difficulty in counting the number of eggs. Currently, calculating the number of eggs is still done manually, which is less than optimal and prone to errors, so many entrepreneurs often experience losses. The manual system currently used also has the potential for this to happen. The use of technology on an MSME scale among laying hen breeders has not been widely adopted, this is due to limited access and understanding of technology. One alternative solution to deal with this problem is to build a real-time computerized system. The system that will currently be built in this research uses GLCM feature extraction and the SVM classification method. This system will detect egg production via CCTV cameras and will be stored in a database to be displayed on the website. The advantage of this system is that egg entrepreneurs can monitor chicken egg yields in real time. The results of trials that have been carried out using GLCM feature extraction and the SVM classification method in calculating the number of eggs using the SVM method with a polynomial kernel are highly recommended for use in this research because it can achieve 95% accuracy.

References

Cirua, A. A. A., Cokrowibowo, S., & Rustan, M. F. (2020). Implementation of Connected Component Labelling for Calculation Amount of Eggs on the Laying Pullet Cage. IOP Conference Series: Materials Science and Engineering, 875(1). https://doi.org/10.1088/1757-899X/875/1/012090

Dewantoro, R. W., Arfan, S. Y. N., & Rizal, R. A. (2022). Analysis Of Right And Wrong Use Of Mask Based On Deep Learning. Journal of Informatics and Telecommunication Engineering, 6(1), 336–343. https://doi.org/10.31289/jite.v6i1.7582

Fadchar, N. A., & Dela Cruz, J. C. (2020). Prediction Model for Chicken Egg Fertility Using Artificial Neural Network. 2020 IEEE 7th International Conference on Industrial Engineering and Applications, ICIEA 2020, 916–920. https://doi.org/10.1109/ICIEA49774.2020.9101966

Muhathir, Rizal, R. A., Sihotang, J. S., & Gultom, R. (2019). Comparison of SURF and HOG extraction in classifying the blood image of malaria parasites using SVM. 2019 International Conference of Computer Science and Information Technology, ICoSNIKOM 2019. https://doi.org/10.1109/ICoSNIKOM48755.2019.9111647

Narushin, V. G., Romanov, M. N., Lu, G., Cugley, J., & Griffin, D. K. (2020). Digital imaging assisted geometry of chicken eggs using Hügelschäffer’s model. Biosystems Engineering, 197, 45–55. https://doi.org/10.1016/j.biosystemseng.2020.06.008

Nyalala, I., Okinda, C., Kunjie, C., Korohou, T., Nyalala, L., & Chao, Q. (2021). Weight and volume estimation of poultry and products based on computer vision systems: a review. Poultry Science, 100(5), 101072. https://doi.org/10.1016/j.psj.2021.101072

Rizal, R. A., Girsang, I. S., & Prasetiyo, S. A. (2019). Klasifikasi Wajah Menggunakan Support Vector Machine (SVM). Remik, 3(2), 275–280. https://doi.org/10.33480/pilar.v15i2.693

Rizal, R. A., Gulo, S., Sihombing, O. D. C., Napitupulu, A. B. M., Gultom, A. Y., & Siagian, T. J. (2019). Analisis Gray Level Co-Occurrence Matrix (Glcm) Dalam Mengenali Citra Ekspresi Wajah. Jurnal Mantik, 3(January), 31–38. http://iocscience.org/ejournal/index.php/mantik/article/view/497/302

Rizal, R. A., & HS, C. (2019). Analysis of Facial Image Extraction on Facial Recognition using Kohonen SOM for UNPRI SIAKAD Online User Authentication. SinkrOn, 4(1), 171. https://doi.org/10.33395/sinkron.v4i1.10242

Rizal, R. A., Susanto, M., & Chandra, A. (2020). Classification Of Borax Content In Tomato Sauce Through Images Using GLCM. SinkrOn, 4(2), 6. https://doi.org/10.33395/sinkron.v4i2.10508

Saifullah, S., & Andiko Putro Suryotomo. (2021). Chicken Egg Fertility Identification using FOS and BP-Neural Networks on Image Processing. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(5), 919–926. https://doi.org/10.29207/resti.v5i5.3431

Saifullah, S., Drezewski, R., Khaliduzzaman, A., Tolentino, L. K., & Ilyos, R. (2022). K-Means Segmentation Based-on Lab Color Space for Embryo Detection in Incubated Egg. Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika, 8(2), 175. https://doi.org/10.26555/jiteki.v8i2.23724

Saifullah, S., Mehriddinovich, R. I., & Tolentino, L. K. (2021). Chicken Egg Detection Based-on Image Processing Concept: A Review. Computing and Information Processing Letters, 1(1), 31. https://doi.org/10.31315/cip.v1i1.6129

Sandy, C. L. M., Efendi, S., & Sembiring, R. W. (2019). Development of Binary Similarity and Distance Measures (BSDM) Algorithm for the Bond of High Development System of Video. Journal of Physics: Conference Series, 1361(1). https://doi.org/10.1088/1742-6596/1361/1/012023

Sandy, C. L. M., Simanjuntak, T. I., Sembiring, A. P., Rizal, R. A., & Fahmi, O. R. (2023). Recognition Of Realtime Based Primitive Geometry Objects Using Perceptron Network. 20(1), 1–7.

Yennimar, Rizal, R. A., Husein, A. M., & Harahap, M. (2019). Sentiment analysis for opinion IESM product with recurrent neural network approach based on long short term memory. 2019 International Conference of Computer Science and Information Technology, ICoSNIKOM 2019, December 2020. https://doi.org/10.1109/ICoSNIKOM48755.2019.9111516

Published
2023-09-30
How to Cite
Sandy, C., Husna, A., Rizal, R., & 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
Article Metrics

Abstract viewed = 110 times
PDF downloaded = 127 times

Most read articles by the same author(s)