EVALUATING OF DEEP LEARNING MODELS FOR EARLY DETECTION IN MEAT CLASSIFICATION: A STUDY ON BEEF AND PORK DETECTION
Abstrak
Accurate classification of beef and pork images is crucial for developing reliable automated food inspection systems, particularly due to their visual similarity in color, texture, and muscle fiber patterns. This study aims to comparatively evaluate the performance of multiple deep learning models for binary meat image classification using RGB digital images. Four Convolutional Neural Network (CNN) architectures, namelyInceptionV3, VGG16, ResNet50, and Xception were assessed under identical preprocessing pipelines and hyperparameter settings to ensure a fair comparison. The dataset underwent cropping, resizing to 224×224 pixels, normalization, and augmentation to enhance variability and improve generalization performance. Model effectiveness was measured using accuracy, precision, recall, and F1-score on unseen test data. Experimental results show that InceptionV3 achieved the most balanced classification performance, with a test accuracy of 72% and an F1-score of 0.7. Although Xception obtained higher training accuracy, it exhibited overfitting during testing, while VGG16 and ResNet50 demonstrated comparatively lower classification capability. These findings indicate that InceptionV3 provides a more stable and generalizable architecture for beef and pork image classification. The study highlights the importance of cross-architecture evaluation in developing robust CNN-based systems for automated meat classification.
Referensi
[1] E. F. Santika, “Deretan Negara yang Paling Banyak Memasok Daging untuk Dikonsumsi Masyarakatnya,” Katadata Media Network. Accessed: Feb. 23, 2026. [Online]. Available: https://databoks.katadata.co.id/agroindustri/statistik/ecc30944b550492/deretan-negara-yang-paling-banyak-memasok-daging-untuk-dikonsumsi-masyarakatnya
[2] S. Bila, A. Fitrianto, and B. Sartono, “Image Classi-fication of Beef and Pork Using Convolutional Neural Network in Keras Framework,” Int. J. Sci. Eng. Inf. Technol., vol. 5, no. 02, pp. 245–248, Jul. 2021, doi: 10.21107/ijseit.v5i02.9864.
[3] N. Afrianto and I. Meiditra, “Implementation of The Weighted K-Nearest Neighbors Algorithm in The Classification of Beef and Pork Images,” J. Inform. Inf. Syst. Softw. Eng. Appl. INISTA, vol. 7, no. 1, pp. 13–21, Oct. 2024.
[4] A. Julianto and A. Kurnialan, “Waspadai Praktik Pen-imbunan dan Daging Oplosan Jelang Ramadhan,” Re-publika. Accessed: Feb. 23, 2026. [Online]. Availa-ble: https://rejabar.republika.co.id/berita/rrnqkp396/waspadai-praktik-penimbunan-dan-daging-oplosan-jelang-ramadhan?
[5] T. Hidayat, “Identifikasi Morfologi Citra Daging Menggunakan Teknik Pengolahan Citra Digital,” JATI J. Mhs. Tek. Inform., vol. 9, no. 1, pp. 1580–1586, Jan. 2025, doi: 10.36040/jati.v9i1.12285.
[6] B. Büyükarıkan, “ConvColor DL: Concatenated con-volutional and handcrafted color features fusion for beef quality identification,” Food Chem., vol. 460, pp. 140795–140795, Dec. 2024, doi: 10.1016/j.foodchem.2024.140795.
[7] M. Gao, C. Song, Q. Zhang, X. Zhang, Y. Li, and F. Yuan, “Research Progress on Color Image Quality Assessment,” J. Imaging, vol. 11, no. 9, pp. 307–307, Sep. 2025, doi: 10.3390/jimaging11090307.
[8] M. Abd Elfattah, A. A. Ewees, A. Darwish, and A. E. Hassanien, “Detection and classification of meat freshness using an optimized deep learning method,” Food Chem., vol. 489, pp. 144783–144783, Oct. 2025, doi: 10.1016/j.foodchem.2025.144783.
[9] K. Anwar and S. Setyowibowo, “The Identification of Beef and Pork Using Neural Network Based on Tex-ture Features,” J. Eng. Res., Sep. 2022, doi: 10.36909/jer.17983.
[10] E. Jo, Y. Lee, Y. Lee, J. Baek, and J. G. Kim, “Rap-id identification of counterfeited beef using deep learn-ing-aided spectroscopy: Detecting colourant and cur-ing agent adulteration,” Food Chem. Toxicol., vol. 181, pp. 114088–114088, Nov. 2023, doi: 10.1016/j.fct.2023.114088.
[11] D. N. Gonçalves et al., “Carcass image segmentation using CNN-based methods,” Inf. Process. Agric., vol. 8, no. 4, pp. 560–572, Dec. 2021, doi: 10.1016/j.inpa.2020.11.004.
[12] M. Chen et al., “Improved faster R-CNN for fabric defect detection based on Gabor filter with Genetic Algorithm optimization,” Comput. Ind., vol. 134, pp. 103551–103551, Jan. 2022, doi: 10.1016/j.compind.2021.103551.
[13] S. S, G. R, and A. R. S M, “Integrated RF-CNN-GRU ensemble for enhanced beef quality classifica-tion: A multi-modal approach,” J. Food Compos. Anal., vol. 134, pp. 106503–106503, Oct. 2024, doi: 10.1016/j.jfca.2024.106503.
[14] C. Park and E. Song, “Ultra-Fast AR Grading Sys-tem: Real-Time Beef Quality Grading With Micro At-tention U-Net and VGG-16,” IEEE Access, vol. 13, pp. 50244–50259, 2025, doi: 10.1109/ACCESS.2025.3551547.
[15] W. B. Zulfikar, A. Angelyna, M. Irfan, A. R. Atmad-ja, and J. Jumadi, “A Deep Learning Approach Using VGG16 to Classify Beef and Pork Images,” JOIV Int. J. Inform. Vis., vol. 9, no. 2, pp. 568–568, Mar. 2025, doi: 10.62527/joiv.9.2.2848.
[16] A. Bajpai, H. Rai, and N. Tiwari, “An Efficient CNN-based Method for Classification of Red Meat Based on its Freshness,” in Intelligent Systems, vol. 728, S. K. Udgata, S. Sethi, and X.-Z. Gao, Eds., in Lecture Notes in Networks and Systems, vol. 728. , Singapore: Springer Nature Singapore, 2024, pp. 393–405. doi: 10.1007/978-981-99-3932-9_34.
[17] S. Rossi et al., “Agri-food traceability today: Advanc-ing innovation towards efficiency, sustainability, ethi-cal sourcing, and safety in food supply chains,” Trends Food Sci. Technol., vol. 163, pp. 105154–105154, Sep. 2025, doi: 10.1016/j.tifs.2025.105154.
[18] K. R. Espina and P. Naval, “1D-CNN Model for Fast Assessment of Beef Freshness from Noisy Electronic Nose Signal,” in 2024 6th International Conference on Computer Communication and the Internet (ICCCI), Tokyo, Japan: IEEE, Jun. 2024, pp. 67–71. doi: 10.1109/ICCCI62159.2024.10674182.
[19] M. Singla, K. Singh Gill, R. Chauhan, H. S. Pokhariya, and G. Sunil, “The Use of MobileNetV2 for Bag Classification Visualisation of a CNN model based on Deep Learning,” in 2024 IEEE 9th Interna-tional Conference for Convergence in Technology (I2CT), Pune, India: IEEE, Apr. 2024, pp. 1–4. doi: 10.1109/I2CT61223.2024.10543660.
[20] F. S. Pranata, A. M. Adif, and J. Na’am, “Automatic Feature Extraction of Marble Fleck in Digital Beef Images to Support Decision Preferences,” JOIV Int. J. Inform. Vis., vol. 9, no. 1, pp. 97–97, Jan. 2025, doi: 10.62527/joiv.9.1.2813.
[21] R. Zhang et al., “Beef quality dual-label classification incorporating texture and multihead map attention mechanisms,” J. Food Compos. Anal., vol. 141, pp. 107360–107360, May 2025, doi: 10.1016/j.jfca.2025.107360.
[22] J. H. Lee, J. Yu, X. Li, D. J. Kim, and S. Park, “Comprehensive non-destructive assessment of beef quality: Focus on textural feature analysis and tender-ness using AI and hyperspectral imaging,” LWT, vol. 231, pp. 118322–118322, Sep. 2025, doi: 10.1016/j.lwt.2025.118322.
[23] J. Huang, H. He, R. Lv, G. Zhang, Z. Zhou, and X. Wang, “Non-destructive detection and classification of textile fibres based on hyperspectral imaging and 1D-CNN,” Anal. Chim. Acta, vol. 1224, pp. 340238–340238, Sep. 2022, doi: 10.1016/j.aca.2022.340238.
[24] T. Hidayat, D. U. E. Saputri, and F. Aziz, “Meat Image Classification Using Deep Learning with Res-Net152V2 Architecture,” J. Techno Nusa Mandiri, vol. 19, no. 2, pp. 131–140, Sep. 2022, doi: 10.33480/techno.v19i2.3932.
[25] K. Agrawal, P. Goktas, M. Holtkemper, C. Beecks, and N. Kumar, “AI-driven transformation in food manufacturing: a pathway to sustainable efficiency and quality assurance,” Front. Nutr., vol. 12, Mar. 2025, doi: 10.3389/fnut.2025.1553942.
[26] X. Tang et al., “Quantification and visualization of meat quality traits in pork using hyperspectral imag-ing,” Meat Sci., vol. 196, pp. 109052–109052, Feb. 2023, doi: 10.1016/j.meatsci.2022.109052.
[27] Z. Daher, M. Mohamadin, A. Rama, A. S. S. Al-bedwawi, H. M. Mahaba, and S. A. Al Taher, “Meat Adulteration in the MENA and GCC Regions: A Scoping Review of Risks, Detection Technologies, and Regulatory Challenges,” Foods, vol. 14, no. 21, pp. 3743–3743, Oct. 2025, doi: 10.3390/foods14213743.
[28] H.-D. Lin, Y.-T. Hsieh, and C.-H. Lin, “Smartphone-Based Sensing System for Identifying Artificially Marbled Beef Using Texture and Color Analysis to Enhance Food Safety,” Sensors, vol. 25, no. 14, pp. 4440–4440, Jul. 2025, doi: 10.3390/s25144440.
[29] Y. Ma, M. Schlangen, J. Potappel, L. Zhang, and A. J. van der Goot, “Quantitative characterizations of visual fibrousness in meat analogues using automated image analysis,” J. Texture Stud., vol. 55, no. 1, Feb. 2024, doi: 10.1111/jtxs.12806.
[30] I. Saraswati, R. Fahrizal, A. N. Fauzan, and M. A. S. Yudono, “Classification of Beef, Goat, and Pork us-ing GLCM Texture-Based Backpropagation Neural Network,” SISTEMASI, vol. 13, no. 6, pp. 2698–2698, Nov. 2024, doi: 10.32520/stmsi.v13i6.4715.
[31] X. Yang et al., “IMF-DeepMarble: Deep learning for precise marbling grade prediction and intramuscular fat content analysis in pork quality assessment,” J. Agric. Food Res., vol. 23, pp. 102251–102251, Oct. 2025, doi: 10.1016/j.jafr.2025.102251.
[32] C. Wakholi et al., “Deep learning feature extraction for image-based beef carcass yield estimation,” Bio-syst. Eng., vol. 218, pp. 78–93, Jun. 2022, doi: 10.1016/j.biosystemseng.2022.04.008.
[33] A. Taufiq Akbar, S. Saifullah, H. Prapcoyo, B. Yuwono, and H. C. Rustamaji, “Robust Classifica-tion of Beef and Pork Images Using EfficientNet B0 Feature Extraction and Ensemble Learning with Visu-al Interpretation,” Regist. J. Ilm. Teknol. Sist. Inf., vol. 11, no. 1, Jun. 2025, doi: 10.26594/register.v11i1.4045.
[34] T. M. Omoniyi et al., “The Effect of Data Augmenta-tion on Performance of Custom and Pre-Trained CNN Models for Crack Detection,” Appl. Sci., vol. 15, no. 22, pp. 12321–12321, Nov. 2025, doi: 10.3390/app152212321.
[35] E. Goceri, “Medical image data augmentation: tech-niques, comparisons and interpretations,” Artif. Intell. Rev., vol. 56, no. 11, pp. 12561–12605, Nov. 2023, doi: 10.1007/s10462-023-10453-z.
[36] A. M. Abdulghani, M. M. Abdulghani, W. L. Wal-ters, and K. H. Abed, “Multiple Data Augmentation Strategy for Enhancing the Performance of YOLOv7 Object Detection Algorithm,” J. Artif. Intell., vol. 5, no. 0, pp. 15–30, 2023, doi: 10.32604/jai.2023.041341.
[37] T. Hidayat, N. Khasanah, E. Firasari, L. Kurniawati, and E. H. Hermaliani, “Comparative Evaluation of CNN Architectures for Skin Cancer Classification,” Int. J. Adv. Comput. Sci. Appl., vol. 16, no. 10, 2025, doi: 10.14569/IJACSA.2025.0161007.
[38] D. Purnamasari, K. Bachrudin, D. H. Suryana, and R. Robert, “Classification of meat using the convolu-tional neural network,” IAES Int. J. Artif. Intell. IJ-AI, vol. 12, no. 4, pp. 1845–1845, Dec. 2023, doi: 10.11591/ijai.v12.i4.pp1845-1853.
[39] K. Kiswanto, H. Hadiyanto, and E. Sediyono, “Meat Texture Image Classification Using the Haar Wavelet Approach and a Gray-Level Co-Occurrence Matrix,” Appl. Syst. Innov., vol. 7, no. 3, pp. 49–49, Jun. 2024, doi: 10.3390/asi7030049.
[40] S. GC et al., “Using Deep Learning Neural Network in Artificial Intelligence Technology to Classify Beef Cuts,” Front. Sens., vol. 2, Jun. 2021, doi: 10.3389/fsens.2021.654357.
[41] Y. Yu, W. Chen, H. Zhang, R. Liu, and C. Li, “Dis-crimination among Fresh, Frozen–Stored and Frozen–Thawed Beef Cuts by Hyperspectral Imaging,” Foods, vol. 13, no. 7, pp. 973–973, Mar. 2024, doi: 10.3390/foods13070973.
[42] A. Mjahad, A. Polo-Aguado, L. Llorens-Serrano, and A. Rosado-Muñoz, “Optimizing Image Feature Ex-traction with Convolutional Neural Networks for Chicken Meat Detection Applications,” Appl. Sci., vol. 15, no. 2, pp. 733–733, Jan. 2025, doi: 10.3390/app15020733.
[43] S. Stendafity, A. M. Hatta, I. C. Setiadi, Sekartedjo, and A. Rahmadiansah, “Minced Meat Classification using Digital Imaging System Coupled with Machine Learning,” presented at the 2023 International Confer-ence on Advanced Mechatronics, Intelligent Manufac-ture and Industrial Automation (ICAMIMIA), IEEE, Nov. 2023, pp. 804–808. doi: 10.1109/ICAMIMIA60881.2023.10427687.
[44] O. Jarkas et al., “ResNet and Yolov5-enabled non-invasive meat identification for high-accuracy box la-bel verification,” Eng. Appl. Artif. Intell., vol. 125, pp. 106679–106679, Oct. 2023, doi: 10.1016/j.engappai.2023.106679.
[45] M. A. Hanif, M. Khadimul Islam Zim, and H. Kaur, “ResNet vs Inception-v3 vs SVM: A Comparative Study of Deep Learning Models for Image Classifica-tion of Plant Disease Detection,” in 2024 IEEE Inter-national Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), Gwalior, India: IEEE, Mar. 2024, pp. 1–6. doi: 10.1109/IATMSI60426.2024.10502832.
[46] C. Tejada, G. Espinoza, and D. Subauste, “Use of Xception Architecture for the Classification of Skin Lesions,” J. Syst. Cybern. Inform., vol. 22, no. 3, pp. 20–25, Jun. 2024, doi: 10.54808/JSCI.22.03.20.
[47] S. A. Farooq, G. Kaur, P. K. Sarao, and R. B. Shar-ma, “The Common Rust Detection in Corn by Utili-zation of Hybrid Deep Learning Algorithm VGG-16,” in 2024 IEEE Pune Section International Conference (PuneCon), Pune, India: IEEE, Dec. 2024, pp. 1–7. doi: 10.1109/PuneCon63413.2024.10894808.
[48] J. Sun, F. Yang, J. Cheng, S. Wang, and L. Fu, “Nondestructive identification of soybean protein in minced chicken meat based on hyperspectral imaging and VGG16-SVM,” J. Food Compos. Anal., vol. 125, p. 105713, Jan. 2024, doi: 10.1016/j.jfca.2023.105713.
[49] S. E. Abdallah, W. M. Elmessery, M. Y. Shams, N. S. A. Al-Sattary, A. A. Abohany, and M. Thabet, “Deep learning model based on ResNet-50 for beef quality classification,” Inf Sci Lett, vol. 12, no. 1, pp. 289–297, 2023.
[50] P. Kumar, Pooja, N. Chauhan, and N. Chaurasia, “A Vision-Based Pothole Detection Using CNN Model,” SN Comput. Sci., vol. 4, no. 6, pp. 716–716, Sep. 2023, doi: 10.1007/s42979-023-02153-w.
[51] R. Archana and P. S. E. Jeevaraj, “Deep learning models for digital image processing: a review,” Artif. Intell. Rev., vol. 57, no. 1, pp. 11–11, Jan. 2024, doi: 10.1007/s10462-023-10631-z.
[52] K. K. Manoj Doss and J. Chen, “Utilizing deep learn-ing techniques to improve image quality and noise re-duction in preclinical low‐dose PET images in the si-nogram domain,” Med. Phys., vol. 51, no. 1, pp. 209–223, Jan. 2024, doi: 10.1002/mp.16830.
[53] N. Merlina, A. Prasetio, I. Zuniarti, N. A. Mayangky, D. N. Sulistyowati, and F. Aziz, “Deep CNN Models for Detecting Cervical Cancer in Pap Smear Images,” TEM J., pp. 1073–1083, May 2025, doi: 10.18421/TEM142-09.
[54] S. Bila, A. Fitrianto, and B. Sartono, “Image Classi-fication of Beef and Pork Using Convolutional Neural Network in Keras Framework,” Int. J. Sci. Eng. Inf. Technol., vol. 5, no. 02, pp. 245–248, Jul. 2021, doi: 10.21107/ijseit.v5i02.9864.
[55] N. Merlina, E. Noersasongko, P. N. Andono, M. A. Soeleman, D. Riana, and J. Na`am, “Medical Image Registration at Pap Smear for Early Identification of Cervical Cancer,” TEM J., pp. 726–731, May 2023, doi: 10.18421/TEM122-16.
[56] N. Merlina, E. Noersasongko, P. N. Andono, M. A. Soeleman, and D. Riana, “Optimization of the Pre-processing Method for Edge Detection on Overlapping Cells at PAP Smear Images,” JOIV Int. J. Inform. Vis., vol. 7, no. 2, p. 471, May 2023, doi: 10.30630/joiv.7.2.1329.
[57] L. Bahmani, S. Minaei, A. Mahdavian, A. Banakar, and M. S. Firouz, “A comparative study of convolu-tional neural networks and traditional feature extrac-tion techniques for adulteration detection in ground beef,” Sens. Bio-Sens. Res., vol. 48, Jun. 2025, doi: 10.1016/j.sbsr.2025.100774.
[58] M. J. de Melo et al., “Automatic segmentation of cattle rib-eye area in ultrasound images using the UNet++ deep neural network,” Comput. Electron. Agric., vol. 195, pp. 106818–106818, Apr. 2022, doi: 10.1016/j.compag.2022.106818.
[59] A. Choompol et al., “Evaluating Optimal Deep Learning Models for Freshness Assessment of Silver Barb Through Technique for Order Preference by Sim-ilarity to Ideal Solution with Linear Programming,” Computers, vol. 14, no. 3, pp. 105–105, Mar. 2025, doi: 10.3390/computers14030105.
[60] A. Arsalane, A. Klilou, and N. E. Barbri, “Perfor-mance evaluation of machine learning algorithms for meat freshness assessment,” Int. J. Electr. Comput. Eng. IJECE, vol. 14, no. 5, pp. 5858–5858, Oct. 2024, doi: 10.11591/ijece.v14i5.pp5858-5865.
[61] W. Jia, S. van Ruth, N. Scollan, and A. Koidis, “Hyperspectral Imaging (HSI) for meat quality evalua-tion across the supply chain: Current and future trends,” Curr. Res. Food Sci., vol. 5, pp. 1017–1027, 2022, doi: 10.1016/j.crfs.2022.05.016.
[62] F. K. G. Schreuders, M. Schlangen, K. Kyriakopou-lou, R. M. Boom, and A. J. van der Goot, “Texture methods for evaluating meat and meat analogue struc-tures: A review,” Food Control, vol. 127, pp. 108103–108103, Sep. 2021, doi: 10.1016/j.foodcont.2021.108103.
[63] Y. T. Mazola, E. A. D. N. Fernandes, G. A. Sarriés, M. A. Bacchi, and C. L. Gonzaga, “Authentication of beef cuts by multielement and machine learning ap-proaches,” J. Trace Elem. Med. Biol., vol. 78, p. 127164, Jul. 2023, doi: 10.1016/j.jtemb.2023.127164.
[64] W. Barragán et al., “Authentication of barley-finished beef using visible and near infrared spectroscopy (Vis-NIRS) and different discrimination approaches,” Meat Sci., vol. 172, p. 108342, Feb. 2021, doi: 10.1016/j.meatsci.2020.108342.
[65] M. A. Siddiqui et al., “Multivariate Analysis Cou-pled with M-SVM Classification for Lard Adultera-tion Detection in Meat Mixtures of Beef, Lamb, and Chicken Using FTIR Spectroscopy,” Foods, vol. 10, no. 10, pp. 2405–2405, Oct. 2021, doi: 10.3390/foods10102405.
[2] S. Bila, A. Fitrianto, and B. Sartono, “Image Classi-fication of Beef and Pork Using Convolutional Neural Network in Keras Framework,” Int. J. Sci. Eng. Inf. Technol., vol. 5, no. 02, pp. 245–248, Jul. 2021, doi: 10.21107/ijseit.v5i02.9864.
[3] N. Afrianto and I. Meiditra, “Implementation of The Weighted K-Nearest Neighbors Algorithm in The Classification of Beef and Pork Images,” J. Inform. Inf. Syst. Softw. Eng. Appl. INISTA, vol. 7, no. 1, pp. 13–21, Oct. 2024.
[4] A. Julianto and A. Kurnialan, “Waspadai Praktik Pen-imbunan dan Daging Oplosan Jelang Ramadhan,” Re-publika. Accessed: Feb. 23, 2026. [Online]. Availa-ble: https://rejabar.republika.co.id/berita/rrnqkp396/waspadai-praktik-penimbunan-dan-daging-oplosan-jelang-ramadhan?
[5] T. Hidayat, “Identifikasi Morfologi Citra Daging Menggunakan Teknik Pengolahan Citra Digital,” JATI J. Mhs. Tek. Inform., vol. 9, no. 1, pp. 1580–1586, Jan. 2025, doi: 10.36040/jati.v9i1.12285.
[6] B. Büyükarıkan, “ConvColor DL: Concatenated con-volutional and handcrafted color features fusion for beef quality identification,” Food Chem., vol. 460, pp. 140795–140795, Dec. 2024, doi: 10.1016/j.foodchem.2024.140795.
[7] M. Gao, C. Song, Q. Zhang, X. Zhang, Y. Li, and F. Yuan, “Research Progress on Color Image Quality Assessment,” J. Imaging, vol. 11, no. 9, pp. 307–307, Sep. 2025, doi: 10.3390/jimaging11090307.
[8] M. Abd Elfattah, A. A. Ewees, A. Darwish, and A. E. Hassanien, “Detection and classification of meat freshness using an optimized deep learning method,” Food Chem., vol. 489, pp. 144783–144783, Oct. 2025, doi: 10.1016/j.foodchem.2025.144783.
[9] K. Anwar and S. Setyowibowo, “The Identification of Beef and Pork Using Neural Network Based on Tex-ture Features,” J. Eng. Res., Sep. 2022, doi: 10.36909/jer.17983.
[10] E. Jo, Y. Lee, Y. Lee, J. Baek, and J. G. Kim, “Rap-id identification of counterfeited beef using deep learn-ing-aided spectroscopy: Detecting colourant and cur-ing agent adulteration,” Food Chem. Toxicol., vol. 181, pp. 114088–114088, Nov. 2023, doi: 10.1016/j.fct.2023.114088.
[11] D. N. Gonçalves et al., “Carcass image segmentation using CNN-based methods,” Inf. Process. Agric., vol. 8, no. 4, pp. 560–572, Dec. 2021, doi: 10.1016/j.inpa.2020.11.004.
[12] M. Chen et al., “Improved faster R-CNN for fabric defect detection based on Gabor filter with Genetic Algorithm optimization,” Comput. Ind., vol. 134, pp. 103551–103551, Jan. 2022, doi: 10.1016/j.compind.2021.103551.
[13] S. S, G. R, and A. R. S M, “Integrated RF-CNN-GRU ensemble for enhanced beef quality classifica-tion: A multi-modal approach,” J. Food Compos. Anal., vol. 134, pp. 106503–106503, Oct. 2024, doi: 10.1016/j.jfca.2024.106503.
[14] C. Park and E. Song, “Ultra-Fast AR Grading Sys-tem: Real-Time Beef Quality Grading With Micro At-tention U-Net and VGG-16,” IEEE Access, vol. 13, pp. 50244–50259, 2025, doi: 10.1109/ACCESS.2025.3551547.
[15] W. B. Zulfikar, A. Angelyna, M. Irfan, A. R. Atmad-ja, and J. Jumadi, “A Deep Learning Approach Using VGG16 to Classify Beef and Pork Images,” JOIV Int. J. Inform. Vis., vol. 9, no. 2, pp. 568–568, Mar. 2025, doi: 10.62527/joiv.9.2.2848.
[16] A. Bajpai, H. Rai, and N. Tiwari, “An Efficient CNN-based Method for Classification of Red Meat Based on its Freshness,” in Intelligent Systems, vol. 728, S. K. Udgata, S. Sethi, and X.-Z. Gao, Eds., in Lecture Notes in Networks and Systems, vol. 728. , Singapore: Springer Nature Singapore, 2024, pp. 393–405. doi: 10.1007/978-981-99-3932-9_34.
[17] S. Rossi et al., “Agri-food traceability today: Advanc-ing innovation towards efficiency, sustainability, ethi-cal sourcing, and safety in food supply chains,” Trends Food Sci. Technol., vol. 163, pp. 105154–105154, Sep. 2025, doi: 10.1016/j.tifs.2025.105154.
[18] K. R. Espina and P. Naval, “1D-CNN Model for Fast Assessment of Beef Freshness from Noisy Electronic Nose Signal,” in 2024 6th International Conference on Computer Communication and the Internet (ICCCI), Tokyo, Japan: IEEE, Jun. 2024, pp. 67–71. doi: 10.1109/ICCCI62159.2024.10674182.
[19] M. Singla, K. Singh Gill, R. Chauhan, H. S. Pokhariya, and G. Sunil, “The Use of MobileNetV2 for Bag Classification Visualisation of a CNN model based on Deep Learning,” in 2024 IEEE 9th Interna-tional Conference for Convergence in Technology (I2CT), Pune, India: IEEE, Apr. 2024, pp. 1–4. doi: 10.1109/I2CT61223.2024.10543660.
[20] F. S. Pranata, A. M. Adif, and J. Na’am, “Automatic Feature Extraction of Marble Fleck in Digital Beef Images to Support Decision Preferences,” JOIV Int. J. Inform. Vis., vol. 9, no. 1, pp. 97–97, Jan. 2025, doi: 10.62527/joiv.9.1.2813.
[21] R. Zhang et al., “Beef quality dual-label classification incorporating texture and multihead map attention mechanisms,” J. Food Compos. Anal., vol. 141, pp. 107360–107360, May 2025, doi: 10.1016/j.jfca.2025.107360.
[22] J. H. Lee, J. Yu, X. Li, D. J. Kim, and S. Park, “Comprehensive non-destructive assessment of beef quality: Focus on textural feature analysis and tender-ness using AI and hyperspectral imaging,” LWT, vol. 231, pp. 118322–118322, Sep. 2025, doi: 10.1016/j.lwt.2025.118322.
[23] J. Huang, H. He, R. Lv, G. Zhang, Z. Zhou, and X. Wang, “Non-destructive detection and classification of textile fibres based on hyperspectral imaging and 1D-CNN,” Anal. Chim. Acta, vol. 1224, pp. 340238–340238, Sep. 2022, doi: 10.1016/j.aca.2022.340238.
[24] T. Hidayat, D. U. E. Saputri, and F. Aziz, “Meat Image Classification Using Deep Learning with Res-Net152V2 Architecture,” J. Techno Nusa Mandiri, vol. 19, no. 2, pp. 131–140, Sep. 2022, doi: 10.33480/techno.v19i2.3932.
[25] K. Agrawal, P. Goktas, M. Holtkemper, C. Beecks, and N. Kumar, “AI-driven transformation in food manufacturing: a pathway to sustainable efficiency and quality assurance,” Front. Nutr., vol. 12, Mar. 2025, doi: 10.3389/fnut.2025.1553942.
[26] X. Tang et al., “Quantification and visualization of meat quality traits in pork using hyperspectral imag-ing,” Meat Sci., vol. 196, pp. 109052–109052, Feb. 2023, doi: 10.1016/j.meatsci.2022.109052.
[27] Z. Daher, M. Mohamadin, A. Rama, A. S. S. Al-bedwawi, H. M. Mahaba, and S. A. Al Taher, “Meat Adulteration in the MENA and GCC Regions: A Scoping Review of Risks, Detection Technologies, and Regulatory Challenges,” Foods, vol. 14, no. 21, pp. 3743–3743, Oct. 2025, doi: 10.3390/foods14213743.
[28] H.-D. Lin, Y.-T. Hsieh, and C.-H. Lin, “Smartphone-Based Sensing System for Identifying Artificially Marbled Beef Using Texture and Color Analysis to Enhance Food Safety,” Sensors, vol. 25, no. 14, pp. 4440–4440, Jul. 2025, doi: 10.3390/s25144440.
[29] Y. Ma, M. Schlangen, J. Potappel, L. Zhang, and A. J. van der Goot, “Quantitative characterizations of visual fibrousness in meat analogues using automated image analysis,” J. Texture Stud., vol. 55, no. 1, Feb. 2024, doi: 10.1111/jtxs.12806.
[30] I. Saraswati, R. Fahrizal, A. N. Fauzan, and M. A. S. Yudono, “Classification of Beef, Goat, and Pork us-ing GLCM Texture-Based Backpropagation Neural Network,” SISTEMASI, vol. 13, no. 6, pp. 2698–2698, Nov. 2024, doi: 10.32520/stmsi.v13i6.4715.
[31] X. Yang et al., “IMF-DeepMarble: Deep learning for precise marbling grade prediction and intramuscular fat content analysis in pork quality assessment,” J. Agric. Food Res., vol. 23, pp. 102251–102251, Oct. 2025, doi: 10.1016/j.jafr.2025.102251.
[32] C. Wakholi et al., “Deep learning feature extraction for image-based beef carcass yield estimation,” Bio-syst. Eng., vol. 218, pp. 78–93, Jun. 2022, doi: 10.1016/j.biosystemseng.2022.04.008.
[33] A. Taufiq Akbar, S. Saifullah, H. Prapcoyo, B. Yuwono, and H. C. Rustamaji, “Robust Classifica-tion of Beef and Pork Images Using EfficientNet B0 Feature Extraction and Ensemble Learning with Visu-al Interpretation,” Regist. J. Ilm. Teknol. Sist. Inf., vol. 11, no. 1, Jun. 2025, doi: 10.26594/register.v11i1.4045.
[34] T. M. Omoniyi et al., “The Effect of Data Augmenta-tion on Performance of Custom and Pre-Trained CNN Models for Crack Detection,” Appl. Sci., vol. 15, no. 22, pp. 12321–12321, Nov. 2025, doi: 10.3390/app152212321.
[35] E. Goceri, “Medical image data augmentation: tech-niques, comparisons and interpretations,” Artif. Intell. Rev., vol. 56, no. 11, pp. 12561–12605, Nov. 2023, doi: 10.1007/s10462-023-10453-z.
[36] A. M. Abdulghani, M. M. Abdulghani, W. L. Wal-ters, and K. H. Abed, “Multiple Data Augmentation Strategy for Enhancing the Performance of YOLOv7 Object Detection Algorithm,” J. Artif. Intell., vol. 5, no. 0, pp. 15–30, 2023, doi: 10.32604/jai.2023.041341.
[37] T. Hidayat, N. Khasanah, E. Firasari, L. Kurniawati, and E. H. Hermaliani, “Comparative Evaluation of CNN Architectures for Skin Cancer Classification,” Int. J. Adv. Comput. Sci. Appl., vol. 16, no. 10, 2025, doi: 10.14569/IJACSA.2025.0161007.
[38] D. Purnamasari, K. Bachrudin, D. H. Suryana, and R. Robert, “Classification of meat using the convolu-tional neural network,” IAES Int. J. Artif. Intell. IJ-AI, vol. 12, no. 4, pp. 1845–1845, Dec. 2023, doi: 10.11591/ijai.v12.i4.pp1845-1853.
[39] K. Kiswanto, H. Hadiyanto, and E. Sediyono, “Meat Texture Image Classification Using the Haar Wavelet Approach and a Gray-Level Co-Occurrence Matrix,” Appl. Syst. Innov., vol. 7, no. 3, pp. 49–49, Jun. 2024, doi: 10.3390/asi7030049.
[40] S. GC et al., “Using Deep Learning Neural Network in Artificial Intelligence Technology to Classify Beef Cuts,” Front. Sens., vol. 2, Jun. 2021, doi: 10.3389/fsens.2021.654357.
[41] Y. Yu, W. Chen, H. Zhang, R. Liu, and C. Li, “Dis-crimination among Fresh, Frozen–Stored and Frozen–Thawed Beef Cuts by Hyperspectral Imaging,” Foods, vol. 13, no. 7, pp. 973–973, Mar. 2024, doi: 10.3390/foods13070973.
[42] A. Mjahad, A. Polo-Aguado, L. Llorens-Serrano, and A. Rosado-Muñoz, “Optimizing Image Feature Ex-traction with Convolutional Neural Networks for Chicken Meat Detection Applications,” Appl. Sci., vol. 15, no. 2, pp. 733–733, Jan. 2025, doi: 10.3390/app15020733.
[43] S. Stendafity, A. M. Hatta, I. C. Setiadi, Sekartedjo, and A. Rahmadiansah, “Minced Meat Classification using Digital Imaging System Coupled with Machine Learning,” presented at the 2023 International Confer-ence on Advanced Mechatronics, Intelligent Manufac-ture and Industrial Automation (ICAMIMIA), IEEE, Nov. 2023, pp. 804–808. doi: 10.1109/ICAMIMIA60881.2023.10427687.
[44] O. Jarkas et al., “ResNet and Yolov5-enabled non-invasive meat identification for high-accuracy box la-bel verification,” Eng. Appl. Artif. Intell., vol. 125, pp. 106679–106679, Oct. 2023, doi: 10.1016/j.engappai.2023.106679.
[45] M. A. Hanif, M. Khadimul Islam Zim, and H. Kaur, “ResNet vs Inception-v3 vs SVM: A Comparative Study of Deep Learning Models for Image Classifica-tion of Plant Disease Detection,” in 2024 IEEE Inter-national Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), Gwalior, India: IEEE, Mar. 2024, pp. 1–6. doi: 10.1109/IATMSI60426.2024.10502832.
[46] C. Tejada, G. Espinoza, and D. Subauste, “Use of Xception Architecture for the Classification of Skin Lesions,” J. Syst. Cybern. Inform., vol. 22, no. 3, pp. 20–25, Jun. 2024, doi: 10.54808/JSCI.22.03.20.
[47] S. A. Farooq, G. Kaur, P. K. Sarao, and R. B. Shar-ma, “The Common Rust Detection in Corn by Utili-zation of Hybrid Deep Learning Algorithm VGG-16,” in 2024 IEEE Pune Section International Conference (PuneCon), Pune, India: IEEE, Dec. 2024, pp. 1–7. doi: 10.1109/PuneCon63413.2024.10894808.
[48] J. Sun, F. Yang, J. Cheng, S. Wang, and L. Fu, “Nondestructive identification of soybean protein in minced chicken meat based on hyperspectral imaging and VGG16-SVM,” J. Food Compos. Anal., vol. 125, p. 105713, Jan. 2024, doi: 10.1016/j.jfca.2023.105713.
[49] S. E. Abdallah, W. M. Elmessery, M. Y. Shams, N. S. A. Al-Sattary, A. A. Abohany, and M. Thabet, “Deep learning model based on ResNet-50 for beef quality classification,” Inf Sci Lett, vol. 12, no. 1, pp. 289–297, 2023.
[50] P. Kumar, Pooja, N. Chauhan, and N. Chaurasia, “A Vision-Based Pothole Detection Using CNN Model,” SN Comput. Sci., vol. 4, no. 6, pp. 716–716, Sep. 2023, doi: 10.1007/s42979-023-02153-w.
[51] R. Archana and P. S. E. Jeevaraj, “Deep learning models for digital image processing: a review,” Artif. Intell. Rev., vol. 57, no. 1, pp. 11–11, Jan. 2024, doi: 10.1007/s10462-023-10631-z.
[52] K. K. Manoj Doss and J. Chen, “Utilizing deep learn-ing techniques to improve image quality and noise re-duction in preclinical low‐dose PET images in the si-nogram domain,” Med. Phys., vol. 51, no. 1, pp. 209–223, Jan. 2024, doi: 10.1002/mp.16830.
[53] N. Merlina, A. Prasetio, I. Zuniarti, N. A. Mayangky, D. N. Sulistyowati, and F. Aziz, “Deep CNN Models for Detecting Cervical Cancer in Pap Smear Images,” TEM J., pp. 1073–1083, May 2025, doi: 10.18421/TEM142-09.
[54] S. Bila, A. Fitrianto, and B. Sartono, “Image Classi-fication of Beef and Pork Using Convolutional Neural Network in Keras Framework,” Int. J. Sci. Eng. Inf. Technol., vol. 5, no. 02, pp. 245–248, Jul. 2021, doi: 10.21107/ijseit.v5i02.9864.
[55] N. Merlina, E. Noersasongko, P. N. Andono, M. A. Soeleman, D. Riana, and J. Na`am, “Medical Image Registration at Pap Smear for Early Identification of Cervical Cancer,” TEM J., pp. 726–731, May 2023, doi: 10.18421/TEM122-16.
[56] N. Merlina, E. Noersasongko, P. N. Andono, M. A. Soeleman, and D. Riana, “Optimization of the Pre-processing Method for Edge Detection on Overlapping Cells at PAP Smear Images,” JOIV Int. J. Inform. Vis., vol. 7, no. 2, p. 471, May 2023, doi: 10.30630/joiv.7.2.1329.
[57] L. Bahmani, S. Minaei, A. Mahdavian, A. Banakar, and M. S. Firouz, “A comparative study of convolu-tional neural networks and traditional feature extrac-tion techniques for adulteration detection in ground beef,” Sens. Bio-Sens. Res., vol. 48, Jun. 2025, doi: 10.1016/j.sbsr.2025.100774.
[58] M. J. de Melo et al., “Automatic segmentation of cattle rib-eye area in ultrasound images using the UNet++ deep neural network,” Comput. Electron. Agric., vol. 195, pp. 106818–106818, Apr. 2022, doi: 10.1016/j.compag.2022.106818.
[59] A. Choompol et al., “Evaluating Optimal Deep Learning Models for Freshness Assessment of Silver Barb Through Technique for Order Preference by Sim-ilarity to Ideal Solution with Linear Programming,” Computers, vol. 14, no. 3, pp. 105–105, Mar. 2025, doi: 10.3390/computers14030105.
[60] A. Arsalane, A. Klilou, and N. E. Barbri, “Perfor-mance evaluation of machine learning algorithms for meat freshness assessment,” Int. J. Electr. Comput. Eng. IJECE, vol. 14, no. 5, pp. 5858–5858, Oct. 2024, doi: 10.11591/ijece.v14i5.pp5858-5865.
[61] W. Jia, S. van Ruth, N. Scollan, and A. Koidis, “Hyperspectral Imaging (HSI) for meat quality evalua-tion across the supply chain: Current and future trends,” Curr. Res. Food Sci., vol. 5, pp. 1017–1027, 2022, doi: 10.1016/j.crfs.2022.05.016.
[62] F. K. G. Schreuders, M. Schlangen, K. Kyriakopou-lou, R. M. Boom, and A. J. van der Goot, “Texture methods for evaluating meat and meat analogue struc-tures: A review,” Food Control, vol. 127, pp. 108103–108103, Sep. 2021, doi: 10.1016/j.foodcont.2021.108103.
[63] Y. T. Mazola, E. A. D. N. Fernandes, G. A. Sarriés, M. A. Bacchi, and C. L. Gonzaga, “Authentication of beef cuts by multielement and machine learning ap-proaches,” J. Trace Elem. Med. Biol., vol. 78, p. 127164, Jul. 2023, doi: 10.1016/j.jtemb.2023.127164.
[64] W. Barragán et al., “Authentication of barley-finished beef using visible and near infrared spectroscopy (Vis-NIRS) and different discrimination approaches,” Meat Sci., vol. 172, p. 108342, Feb. 2021, doi: 10.1016/j.meatsci.2020.108342.
[65] M. A. Siddiqui et al., “Multivariate Analysis Cou-pled with M-SVM Classification for Lard Adultera-tion Detection in Meat Mixtures of Beef, Lamb, and Chicken Using FTIR Spectroscopy,” Foods, vol. 10, no. 10, pp. 2405–2405, Oct. 2021, doi: 10.3390/foods10102405.
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Hidayat, T., Faruq Aziz, Daniati Uki Eka Saputri, & Nurul Khasanah. (2026). EVALUATING OF DEEP LEARNING MODELS FOR EARLY DETECTION IN MEAT CLASSIFICATION: A STUDY ON BEEF AND PORK DETECTION. TEKNIMEDIA: Teknologi Informasi Dan Multimedia, 7(1), 132-145. https://doi.org/10.46764/teknimedia.v7i1.370
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