An Image Dataset Construction for Flower Recognition Using Convolutional Neural Network
DOI:
https://doi.org/10.25271/sjuoz.2020.8.3.747Keywords:
Plant Flower Recognition, Flower Recognition, Flower Identification System, Flower Classification, Convolutional Neural Network, Deep LearningAbstract
Classifying flowers is a difficult activity because of the wide variety of flower species that have similar form. In this paper, a deep learning model for extracting features and classifying different flower types or species developed by using a popular method called Convolutional Neural Network (CNN). The identification system has been evaluated on a new dataset that has been designed in this work that collected flowers from Kurdistan. The dataset contains 1300 images of different flowers, 1040 images (%80) of which used for training purpose and 260 (%20) images used for test purpose, categorized into 26 classes. In addition, Various number of layers, activation function and pooling schemes were implemented to improve the classification rates. The recognition rate of proposed CNN model is 94.61%.
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Prasad, M. V., Lakshmamma, B. J., Chandana, A. H., Komali, K., Manoja, M., Kumar, P. R., . . . Kiran, P. S. (2018). An efficient classification of flower images with convolutional neural networks. International Journal of Engineering & Technology, 7(1.1), 384-391.
Reyes, A. K., Caicedo, J. C., & Camargo, J. E. (2015). Fine-tuning Deep Convolutional Networks for Plant Recognition. CLEF (Working Notes), (pp. 467-475).
S.M.Mukane, & J.A.Kendule. (2013). Flower Classification Using Neural Network Based Image Processing. IOSR Journal of Electronics and Communication Engineering, 7(3), 80-85.
Silva, H., Pinho, R., Lopes, L., Nogueira, A. J., & Silveira, P. (2011). Illustrated plant identification keys: An interactive tool to learn botany. Computers & Education, 56(4), 969–973.
Sun, Y., Liu, Y., Wang, G., & Zhang, H. (217). Deep Learning for Plant Identification in Natural Environment. Computational intelligence and neuroscience, 2017.
Almogdady, H., Manaseer, S., & Hiary, H. (2018). A Flower Recognition System Based On Image Processing And Neural Networks. International Journal Of Scientific & Technology Research, 7(11), 166-173.
Asaf, Z., Siddique, M. U., Akram, M. A., Sabir, M. W., Ali, H. M., & Akram, D. M. (n.d.). Leaf Recognition Using Deep Convolutional Neural Networks.
Bengio, Y. (2009). Learning Deep Architectures for AI (Vol. 2). Foundation and Trends in Machine Learning .
Bengio, Y. (2012). Deep Learning of Representations for Unsupervised and Transfer Learning. JMLR: Workshop and Conference Proceedings, 27, pp. 17-36.
C.Bhanuprakash, GK, P., Karegowda, A. G., & Ramesh, C. (2016). Texture Based Flower Species Classification Using Neural Network. International Journal of Advance Foundation and Research in Computer , 3(6), 2348 – 4853.
Cope, J. S., Corney, D., Clark, J. Y., Remagnino, P., & Wilkin, P. (2012). Plant species identification using digital morphometrics: A review. Expert Systems with Applications, 39(8), 7562–7573.
Du, T., & Shanker, V. K. (2009). Deep Learning for Natural Language Processing. ecis. Udel. Edu, 1-7.
FatihahSahidan, N., Juha, A. K., Mohammad, N., & Ibrahim, Z. (2019). Flower and leaf recognition for plant identification using convolutional neural network. Indonesian Journal of Electrical Engineering and Computer Science, 16(2), 737-743.
Gurnani, A., Mavani, V., Gajjar, V., & Khandhediya, Y. (2017). Flower Categorization using Deep Convolutional Neural Networks. arXiv preprint arXiv:1708.03763, 4321-4324.
Hiary, H., Saadeh, H., Saadeh, M., & Yaqub, M. (2018). Flower classification using deep convolutional neural networks. IET Computer Vision, 12(6), 855-862.
Khan, A., Sohail, A., Zahoora, U., & Qureshi, A. S. (2019). A Survey of the Recent Architectures of Deep Convolutional Neural Networks. arXiv preprint arXiv:1901.06032, 1-70.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
Lee, S. H., Chang, Y. L., Chan, C. S., & Remagnino, P. (2016). Plant Identification System based on a Convolutional Neural Network for the LifeClef 2016 Plant Classification Task. In CLEF (Working Notes), 502-510.
Liua, W., Wanga, Z., Liua, X., Zengb, N., Liuc, Y., & Alsaadid, F. E. (2017). A survey of deep neural network architectures and their applications. Neurocomputing, 234, 11-26.
Prasad, M. V., Lakshmamma, B. J., Chandana, A. H., Komali, K., Manoja, M., Kumar, P. R., . . . Kiran, P. S. (2018). An efficient classification of flower images with convolutional neural networks. International Journal of Engineering & Technology, 7(1.1), 384-391.
Reyes, A. K., Caicedo, J. C., & Camargo, J. E. (2015). Fine-tuning Deep Convolutional Networks for Plant Recognition. CLEF (Working Notes), (pp. 467-475).
S.M.Mukane, & J.A.Kendule. (2013). Flower Classification Using Neural Network Based Image Processing. IOSR Journal of Electronics and Communication Engineering, 7(3), 80-85.
Silva, H., Pinho, R., Lopes, L., Nogueira, A. J., & Silveira, P. (2011). Illustrated plant identification keys: An interactive tool to learn botany. Computers & Education, 56(4), 969–973.
Sun, Y., Liu, Y., Wang, G., & Zhang, H. (217). Deep Learning for Plant Identification in Natural Environment. Computational intelligence and neuroscience, 2017.
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Published
2020-09-30
How to Cite
Omer, S. M., Hasan, R. M., & Anwer, B. N. (2020). An Image Dataset Construction for Flower Recognition Using Convolutional Neural Network. Science Journal of University of Zakho, 8(3), 112–117. https://doi.org/10.25271/sjuoz.2020.8.3.747
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Science Journal of University of Zakho
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