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Journals / Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi / 2026 / Cilt: 40 Sayı: 2

Performance comparison of AlexNet and MobileNetV2 architectures in flower classification

Çiçek Sınıflandırmada AlexNet ve MobileNetV2 mimarilerinin performans karşılaştırması

Gözde Sena Karabay(Fırat Üniversitesi)0000-0001-6640-9245
Mehmet Çavaş(Fırat Üniversitesi)0000-0002-0130-1644
Engin Avcı(Fırat Üniversitesi)0000-0001-6278-3221
Pages
829–836
Publication Date
2025-02-03
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In this study, which was created using convolutional neural networks, the performance comparisons of the deep learning architectures AlexNet and MobileNetV2 architectures were made and a success rate of 93.1% was obtained from the AlexNet architecture and 93.9% from the MobileNetV2 architecture.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The problem of classifying flower species is a challenging process due to the high diversity.
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Key Findings & Data:Oxford-17 data set was used in this study.
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Conclusion & Impact:In this study, which was created using convolutional neural networks, the performance comparisons of the deep learning architectures AlexNet and MobileNetV2 architectures were made and a success rate of 93.1% was obtained from the AlexNet architecture and 93.9% from the MobileNetV2 architecture.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Derin Öğrenme · Çiçek Sınıflandırma · AlexNet

Abstract

The problem of classifying flower species is a challenging process due to the high diversity. Computer vision and deep learning applications provide great advantages to facilitate the work of researchers working in this field. Deep learning methods can achieve high success with the development of new algorithms. These methods, which are used in many fields, also provide successful results in classifying flower species. Oxford-17 data set was used in this study. The data set includes 1360 flower images belonging to 17 classes. In this study, which was created using convolutional neural networks, the performance comparisons of the deep learning architectures AlexNet and MobileNetV2 architectures were made and a success rate of 93.1% was obtained from the AlexNet architecture and 93.9% from the MobileNetV2 architecture.

Özet

Çeşitliliğin fazla olması nedeniyle çiçek türlerini sınıflandırma problemi zorlu bir süreçtir. Bu alanla ilgili çalışmalar yapan araştırmacıların işlerini kolaylaştırmak için bilgisayarlı görü ve derin öğrenme uygulamaları büyük avantaj sağlamaktadır. Derin öğrenme yöntemleri yeni algoritmaların geliştirilmesiyle yüksek başarılara ulaşabilmektedir Birçok alanda kullanılan bu yöntemler çiçek türlerini sınıflandırmada da başarılı sonuçlar vermektedir. Yapılan bu çalışmada Oxford-17 veri seti kullanılmıştır. Veri setinde 17 sınıfa ait 1360 adet çiçek görüntüsü yer almaktadır. Evrişimsel sinir ağları kullanılarak oluşturulan bu çalışmada derin öğrenme mimarilerinden AlexNet ve MobileNetV2 mimarilerinin performans karşılaştırmaları yapılarak AlexNet mimarisinden %93,1, MobileNetV2 mimarisinden %93,9 başarı oranı elde edilmiştir.

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Cite This Article

Karabay, G. S., Çavaş, M., Avcı, E. (2025). Performance comparison of AlexNet and MobileNetV2 architectures in flower classification. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, Cilt: 40 Sayı: 2, 829–836. https://doi.org/10.17341/gazimmfd.1463663

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References (27)

  1. Karabay, G. S., Çavaş, M., Derin Öğrenme Yöntemiyle Alzheimer Hastalığının Tespiti, Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 34 (2), 879–887, 2022.
  2. Bae, H. S., Lee, H. J., Lee, S. G., Voice recognition based on adaptive MFCC and deep learning, 2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA) June 05-07, 2016.
  3. Khurana, L., Chauhan, A., Naved, M., Singh, P., Speech Recognition with Deep Learning, Journal of Physics: Conference Series April, 2021.
  4. Zhang, X., Tao, Z., Zhao, H., Xu, T., Pathological voice recognition by deep neural network, 2017 4th International Conference on Systems and Informatics (ICSAI) November 11-13, 2017.
  5. He, K., Zhang, X., Ren, S., Sun, J., Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, 37 (9), 1904–1916, 2015.
  6. Wu, Z., Shen, C., Van Den Hengel, A., Wider or Deeper: Revisiting the ResNet Model for Visual Recognition, Pattern Recognition, 90, 119–133, 2019.
  7. Lopez, M. M., Kalita, J., Deep Learning applied to NLP, ArXiv Preprint ArXiv:1703.03091, 2017.
  8. Guo, J., He, H., He, T., Lausen, L., Li, M., Lin, H., Shi, X., Wang, C., Xie, J., Zha, S., Zhang, A., Zhang, H., Zhang, Z., Zhang, Z., Zheng, S., Zhu, Y., GluonCV and gluon NLP: Deep learning in computer vision and natural language processing, The Journal of Machine Learning Research, 21 (1), 845–851, 2020.
  9. Giri, S., Image based flower species classification using CNN, Journal of Innovation in Engineering Education, 2 (1), 182–186, 2019.
  10. Lin, D. S., Cheng, C. F., Research on flower image recognition algorithm, International Conference on Big Data, Information and Computer Network (BDICN), IEEE January 20-22, 2022.
  11. Pandey, S., Sindhuja, B., Nagamanjularani, C. S., Nagarajan, S., Exploring Transfer Learning Techniques for Flower Recognition Using CNN, Data Science and Security: Proceedings of IDSCS 2022, Springer Nature Singapore July 02, 2022.
  12. Giri, S., Joshi, B., Transfer Learning Based Image Visualization Using CNN, International Journal of Artificial Intelligence and Applications (IJAIA), 10 (4), 47–55, 2019.
  13. Tian, M., Chen, H., Wang, Q., Flower identification based on Deep Learning, Journal of Physics: Conference Series, 1237 (2), 1-10, 2019.
  14. Zhao, X., Feng, Y., Shi, X., Wang, Y., Zhang, G., A color constancy based flower classification method in the blockchain data lake, Multimedia Tools and Applications, 83 (10), 28657–28673, 2024.
  15. Singprayoon, S., Supratid, S., Effects of Number and Position of Auxiliary Networks used in Inception Convolutional Neural Network on Object Recognition, 2021 International Electrical Engineering Congress, (IEECON 2021), Pattaya March, 2021.
  16. Ran, J., Shi, Y., Yu, J., Li, D., A Multi-Feature Convolution Neural Network for Automatic Flower Recognition, Journal of Circuits, Systems and Computers, 30 (15), 2021.
  17. Krizhevsky, A., Sutskever, I., Hinton, G. E., ImageNet Classification with Deep Convolutional Neural Networks, Advances in Neural Information Processing Systems, 25, 1-9, 2012.
  18. Sandler, M., Howard, A., Zhu, M.; Zhmoginov, A., Chen, L. C., MobileNetV2: Inverted Residuals and Linear Bottlenecks, CVF Conference on Computer Vision and Pattern Recognition, IEEE, 4510-4520, 2018.
  19. Nilsback, M. E., Zisserman, A., A visual vocabulary for flower classification, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06) June 17-22, 2006.
  20. Susanti, H., Yusuf, M., Sumardiono, A., Simulation and Experimentation of Fire Fighting with Early Detection Based on MobileNetV2, 2022 5th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), 548–553, 2022.
  21. Van Hieu, N., Hien, N. L. H., Automatic plant image identification of Vietnamese species using deep learning models, International Journal of Engineering Trends and Technology (IJETT), 68 (4), 25–31, 2020.
  22. Sunnetci, K. M., Ulukaya, S., Alkan, A., Periodontal bone loss detection based on hybrid deep learning and machine learning models with a user-friendly application, Biomedical Signal Processing and Control, 77, 2022.
  23. BİLGİN, G., Investigation of The Risk of Diabetes in Early Period using Machine Learning Algorithms, Journal of Intelligent Systems: Theory and Applications, 4 (1), 55–64, 2021.
  24. Choose Classifier Options - MATLAB & Simulink. https://www.mathworks.com/help/stats/choose-a-classifier.html. Erişim tarihi Haziran 5, 2024.
  25. Sünnetci, K. M., Alkan, A., KNN and Decision Trees based SPPM demodulators applicable to synchronous modulation techniques, Journal of the Faculty of Engineering and Architecture of Gazi University, 37 (3), 1247–1261, 2022.
  26. Chicco, D., Jurman, G., The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation, BMC Genomics, 21 (6), 2020.
  27. Chicco, D., Tötsch, N., Jurman, G., The matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation, BioData Mining, 14 (13), 2021.