Comparative Study: Flower Classification using Deep Learning, SMOTE and Fine-Tuning
Abstract
Deep learning is a technology that can be used to classify flowers. In this research, flower type classification using the CNN method with several existing CNN architectures will be discussed. The data consists of 4317 images in .jpg format, covering 5 classes that is sunflower, dandelion, daisy, tulip and rose. The distribution of data for each class is daisy with 764 pictures, dandelion with 1052 pictures, rose with 784 pictures, sunflower with 733 pictures, and tulip with 984 pictures. With total dataset of 4317 pictures is further split to training data with ratio of 60%, validation with ratio of 10%, and testing with ratio of 30% to process with the CNN method and CNN framework. Due to the imbalance data distribution, the SMOTE method is applied to balancing number of samples in each class. This research compares CNN architectures, including CNN, GoogleNet, DenseNet, and MobileNet, where each transfer learning model undergoes fine-tuning to improve performance. At the classification stage, performance will be measured based on model testing accuracy. The accuracy obtained using CNN is 74.61%, using GoogleNet is 87.45%, DenseNet is 93.92%, and MobileNet is 88.34%.
References
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[9] M. Mileva et al., Rose flowers—a delicate perfume or a natural healer?, Jan. 01, 2021, MDPI AG. doi: 10.3390/biom11010127.
[10] A. Khan, R. Arumugam Senthil, J. Pan, Y. Sun, and X. Liu, Hierarchically Porous Biomass Carbon Derived from Natural Withered Rose Flowers as High-Performance Material for Advanced Supercapacitors, Batter Supercaps, vol. 3, no. 8, pp. 731–737, Aug. 2020, doi: 10.1002/batt.202000046.
[11] O. H. Kwon, H. G. Choi, S. J. Kim, Y. R. Lee, H. H. Jung, and K. Y. Park, Changes in Yield, Quality, and Morphology of Three Grafted Cut Roses Grown in a Greenhouse Year-Round, Horticulturae, vol. 8, no. 7, Jul. 2022, doi: 10.3390/horticulturae8070655.
[12] R. Kaur, A. Jain, and S. Kumar, Optimization classification of sunflower recognition through machine learning, in Materials Today: Proceedings, Elsevier Ltd, 2021, pp. 207–211. doi: 10.1016/j.matpr.2021.05.182.
[13] E. Mladenovic et al., Effect of plant density on stem and flower quality of single-stem ornamental sunflower genotypes, Horticultural Science, vol. 47, no. 1, pp. 45–52, 2020, doi: 10.17221/10/2019-HORTSCI.
[14] D. T. C. Nguyen et al., The sunflower plant family for bioenergy, environmental remediation, nanotechnology, medicine, food and agriculture: a review, Oct. 01, 2021, Springer Science and Business Media Deutschland GmbH. doi: 10.1007/s10311-021-01266-z.
[15] W. Handayati and D. Sihombing, Study of NPK fertilizer effect on sunflower growth and yield, in AIP Conference Proceedings, American Institute of Physics Inc., Jul. 2019. doi: 10.1063/1.5115635.
[16] S. Dong, P. Wang, and K. Abbas, A survey on deep learning and its applications, May 01, 2021, Elsevier Ireland Ltd. doi: 10.1016/j.cosrev.2021.100379.
[17] C. Janiesch, P. Zschech, and K. Heinrich, Machine learning and deep learning, Electronic Markets, vol. 31, pp. 687–694, Apr. 2021, doi: https://doi.org/10.1007/s12525-021-00475-2.
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[19] N. O’Mahony et al., Deep Learning vs. Traditional Computer Vision, in Advances in Intelligent Systems and Computing, Springer Verlag, 2020, pp. 128–144. doi: 10.1007/978-3-030-17795-9_10.
[20] S. Dargan, M. Kumar, M. R. Ayyagari, and G. Kumar, A Survey of Deep Learning and Its Applications: A New Paradigm to Machine Learning, Archives of Computational Methods in Engineering, vol. 27, no. 4, pp. 1071–1092, Sep. 2020, doi: 10.1007/s11831-019-09344-w.
[21] A. Esteva et al., Deep learning-enabled medical computer vision, Dec. 01, 2021, Nature Research. doi: 10.1038/s41746-020-00376-2.
[22] P. Wang, E. Fan, and P. Wang, Comparative analysis of image classification algorithms based on traditional machine learning and deep learning, Pattern Recognit Lett, vol. 141, pp. 61–67, Jan. 2021, doi: 10.1016/j.patrec.2020.07.042.
[23] G. Wang, J. C. Ye, and B. De Man, Deep learning for tomographic image reconstruction, Dec. 01, 2020, Nature Research. doi: 10.1038/s42256-020-00273-z.
[24] H. Liu and B. Lang, Machine learning and deep learning methods for intrusion detection systems: A survey, Oct. 01, 2019, MDPI AG. doi: 10.3390/app9204396.
[25] D. R. Sarvamangala and R. V. Kulkarni, Convolutional neural networks in medical image understanding: a survey, Mar. 01, 2022, Springer Science and Business Media Deutschland GmbH. doi: 10.1007/s12065-020-00540-3.
[26] Y. Li, J. Nie, and X. Chao, Do we really need deep CNN for plant diseases identification?, Comput Electron Agric, vol. 178, Nov. 2020, doi: 10.1016/j.compag.2020.105803.
[27] M. B. Bora, D. Daimary, K. Amitab, and D. Kandar, Handwritten Character Recognition from Images using CNN-ECOC, in Procedia Computer Science, Elsevier B.V., 2020, pp. 2403–2409. doi: 10.1016/j.procs.2020.03.293.
[28] C. Narvekar and M. Rao, Flower classification using CNN and transfer learning in CNN-Agriculture Perspective, in Proceedings of the 3rd International Conference on Intelligent Sustainable Systems, ICISS 2020, Institute of Electrical and Electronics Engineers Inc., Dec. 2020, pp. 660–664. doi: 10.1109/ICISS49785.2020.9316030.
[29] F. BOZKURT, A Study on CNN Based Transfer Learning for Recognition of Flower Species, European Journal of Science and Technology, Jan. 2022, doi: 10.31590/ejosat.1039632.
[30] S. Wang, Y. Dai, J. Shen, and J. Xuan, Research on expansion and classification of imbalanced data based on SMOTE algorithm, Sci Rep, vol. 11, no. 1, Dec. 2021, doi: 10.1038/s41598-021-03430-5.
[31] A. Ishaq et al., Improving the Prediction of Heart Failure Patients’ Survival Using SMOTE and Effective Data Mining Techniques, IEEE Access, vol. 9, pp. 39707–39716, 2021, doi: 10.1109/ACCESS.2021.3064084.
[32] A. Çinar and M. Yildirim, Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture, Med Hypotheses, vol. 139, Jun. 2020, doi: 10.1016/j.mehy.2020.109684.
[33] G. Shrestha, Deepsikha, M. Das, and N. Dey, Plant Disease Detection Using CNN, in Proceedings of 2020 IEEE Applied Signal Processing Conference, ASPCON 2020, Institute of Electrical and Electronics Engineers Inc., Oct. 2020, pp. 109–113. doi: 10.1109/ASPCON49795.2020.9276722.
[34] M. A. Hossain and Md. S. A. Sajib, Classification of Image using Convolutional Neural Network (CNN), Global Journal of Computer Science and Technology: D Neural & Artificial Intelligence, vol. 19, no. 2, 2019, Accessed: Oct. 22, 2024. [Online]. Available: https://globaljournals.org/GJCST_Volume19/2-Classification-of-Image-using-Convolutional.pdf
[35] H. Gholamalinezhad and H. Khosravi, Pooling Methods in Deep Neural Networks, a Review, arXiv preprint, 2020, doi: https://doi.org/10.48550/arXiv.2009.07485.
[36] Z. A. Sejuti and M. S. Islam, An Efficient Method to Classify Brain Tumor using CNN and SVM, in International Conference on Robotics, Electrical and Signal Processing Techniques, 2021, pp. 644–648. doi: 10.1109/ICREST51555.2021.9331060.
[37] L. Chen, X. Kuang, A. Xu, S. Suo, and Y. Yang, A Novel Network Intrusion Detection System Based on CNN, in Proceedings - 2020 8th International Conference on Advanced Cloud and Big Data, CBD 2020, Institute of Electrical and Electronics Engineers Inc., Dec. 2020, pp. 243–247. doi: 10.1109/CBD51900.2020.00051.
[38] S. M. Hassan, A. K. Maji, M. Jasiński, Z. Leonowicz, and E. Jasińska, Identification of plant-leaf diseases using cnn and transfer-learning approach, Electronics (Switzerland), vol. 10, no. 12, Jun. 2021, doi: 10.3390/electronics10121388.
[39] K. Thenmozhi and U. Srinivasulu Reddy, Crop pest classification based on deep convolutional neural network and transfer learning, Comput Electron Agric, vol. 164, Sep. 2019, doi: 10.1016/j.compag.2019.104906.
[40] T. Rahman et al., Transfer learning with deep Convolutional Neural Network (CNN) for pneumonia detection using chest X-ray, Applied Sciences (Switzerland), vol. 10, no. 9, May 2020, doi: 10.3390/app10093233.
[41] N. Aneja and S. Aneja, Transfer Learning using CNN for Handwritten Devanagari Character Recognition, 2019 1st International Conference on Advances in Information Technology (ICAIT), pp. 293–296, Feb. 2019, doi: 10.1109/ICAIT47043.2019.8987286.
[42] K. M. Hosny, M. A. Kassem, and M. M. Foaud, Classification of skin lesions using transfer learning and augmentation with Alex-net, PLoS One, vol. 14, no. 5, May 2019, doi: 10.1371/journal.pone.0217293.
[43] L. Yang et al., GoogLeNet based on residual network and attention mechanism identification of rice leaf diseases, Comput Electron Agric, vol. 204, p. 107543, 2023.
[44] N. Hasan, Y. Bao, A. Shawon, and Y. Huang, DenseNet Convolutional Neural Networks Application for Predicting COVID-19 Using CT Image, SN Comput Sci, vol. 2, no. 5, Sep. 2021, doi: 10.1007/s42979-021-00782-7.
[45] C. Bi, J. Wang, Y. Duan, B. Fu, J.-R. Kang, and Y. Shi, MobileNet based apple leaf diseases identification, Mobile Networks and Applications, pp. 1–9, 2022.
[46] Y. Li, J. Nie, and X. Chao, Do we really need deep CNN for plant diseases identification?, Comput Electron Agric, vol. 178, Nov. 2020, doi: 10.1016/j.compag.2020.105803.
[47] M. Te Wu, Confusion matrix and minimum cross-entropy metrics based motion recognition system in the classroom, Sci Rep, vol. 12, no. 1, Dec. 2022, doi: 10.1038/s41598-022-07137-z.
[48] J. Musaev, A. Anorboev, N. T. Nguyen, and D. Hwang, KeepNMax: Keep N Maximum of Epoch-Channel Ensemble Method for Deep Learning Models, IEEE Access, vol. 11, pp. 9339–9350, 2023, doi: 10.1109/ACCESS.2023.3239658.
[49] G. Wega Intyanto, Klasifikasi Citra Bunga dengan Menggunakan Deep Learning: CNN (Convolution Neural Network), Jurnal Arus Elektro Indonesia, vol. 7, no. 3, pp. 80–83, 2021, doi: https://doi.org/10.19184/jaei.v7i3.28141.
[50] R. Kursun, I. Cinar, Y. S. Taspinar, and M. Koklu, Flower recognition system with optimized features for deep features, in 2022 11th Mediterranean conference on embedded computing (MECO), IEEE, 2022, pp. 1–4.
[51] G. Yifei, Q. Chuxian, X. Jiexiang, M. Yixuan, and T. T. Toe, Flower image classification based on improved convolutional neural network, in 2022 12th International Conference on Information Technology in Medicine and Education (ITME), IEEE, 2022, pp. 81–87.
Published
2024-11-20
How to Cite
PRASKATAMA, Vincentius; SHIDIK, Guruh Fajar; NINGRUM, Amanda Prawita.
Comparative Study: Flower Classification using Deep Learning, SMOTE and Fine-Tuning.
JOURNAL OF APPLIED INFORMATICS AND COMPUTING, [S.l.], v. 8, n. 2, p. 557-568, nov. 2024.
ISSN 2548-6861.
Available at: <http://704209.wb34atkl.asia/index.php/JAIC/article/view/8730>. Date accessed: 28 nov. 2024.
doi: https://doi.org/10.30871/jaic.v8i2.8730.
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