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Derin Öğrenme Algoritmalarının Yaprak Sınıflandırma Başarımlarının Karşılaştırılması

Year 2018, Volume: 1 Issue: 1, 10 - 21, 02.04.2018

Ferdi Doğan , İbrahim Türkoğlu

Abstract

Bu makalede, bitki yapraklarının sınıflandırılması için kullanılan pek çok yönteme karşı derin öğrenme yoluyla sınıflandırılması yapılarak derin öğrenme algoritmalarının başarımları ortaya konulmuştur. Görüntü işleme yöntemleri ile yapılan sınıflandırma işlemlerinde ön işlem, özellik çıkarımı ve sınıflandırma yöntemi aracılığı ile sonuç alınmaktadır. Derin öğrenme yöntemlerinde yapılan işlemlerde bu gibi işlemlere ihtiyaç duyulmamaktadır. Derin öğrenme yöntemlerinde, ön işlem ve özellik çıkarım gibi aşamalar Konvolüsyonel Sinir Ağları aracılığı ile gerçekleştirilmektedir.

Bu çalışmada, yaprak örüntüsü olarak kullanılan veri tabanında, görüntü örnekleri 32 sınıftan oluşan yaklaşık 1900 görüntü vardır. Her bir görüntü sınıfı için ortalama 60 adet görüntü yer almaktadır. Burada yer alan görüntüler yansıma ve tersleme işlemleriyle 4 katına çıkarılmış yaklaşık olarak 7600 görüntü ile işlemler yapılmıştır.

Derin öğrenme yöntemlerinden ise AlexNet, Vgg16, Vgg19, ResNet50, GoogleNet gibi derin öğrenme algoritmaları kullanılmış her bir algoritma için yaprak sınıflandırma uygulaması yapılarak, başarımları değerlendirilmiştir.

Keywords

Derin Öğrenme Evrişimsel Sinir Ağları Yaprak Sınıflandırma Derin Öğrenme Algoritmaları

References

  • Aakif, A., & Khan, M. F. (2015). Automatic classification of plants based on their leaves. Biosystems Engineering, 139, 66-75.
  • Adler, A., Elad, M., & Zibulevsky, M. (2016). Compressed Learning: A Deep Neural Network Approach. arXiv preprint arXiv:1610.09615.
  • Arribas, J. I., Sánchez-Ferrero, G. V., Ruiz-Ruiz, G., & Gómez-Gil, J. (2011). Leaf classification in sunflower crops by computer vision and neural networks. Computers and Electronics in Agriculture, 78(1), 9-18.
  • Bama, B. S., Valli, S. M., Raju, S., & Kumar, V. A. (2011). Content based leaf image retrieval (CBLIR) using shape, color and texture features. Indian Journal of Computer Science and Engineering, 2(2), 202-211.
  • Belhumeur, P. N., Chen, D., Feiner, S., Jacobs, D. W., Kress, W. J., Ling, H., ... & Zhang, L. (2008, October). Searching the world’s herbaria: A system for visual identification of plant species. In European Conference on Computer Vision (pp. 116-129). Springer, Berlin, Heidelberg.
  • Castelluccio, M., Poggi, G., Sansone, C., & Verdoliva, L. (2015). Land use classification in remote sensing images by convolutional neural networks. arXiv preprint arXiv:1508.00092.
  • Chaki, J., Parekh, R., & Bhattacharya, S. (2015). Plant leaf recognition using texture and shape features with neural classifiers. Pattern Recognition Letters, 58, 61-68.
  • Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016). Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. arXiv preprint arXiv:1606.00915.
  • Deng, L., & Yu, D. (2014). Deep learning: methods and applications. Foundations and Trends® in Signal Processing, 7(3–4), 197-387. Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1). Cambridge: MIT press.
  • Gu, X., Du, J. X., & Wang, X. F. (2005, August). Leaf recognition based on the combination of wavelet transform and gaussian interpolation. In International Conference on Intelligent Computing (pp. 253-262). Springer, Berlin, Heidelberg.
  • Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S. (2016). Deep learning for visual understanding: A review. Neurocomputing, 187, 27-48.
  • He, K., Zhang, X., Ren, S., & Sun, J. (2015). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In Proceedings of the IEEE international conference on computer vision (pp. 1026-1034).
  • He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
  • Hebb, D. O. (2005). The organization of behavior: A neuropsychological theory. Psychology Press. Higgins, I., Matthey, L., Glorot, X., Pal, A., Uria, B., Blundell, C., ... & Lerchner, A. (2016).
  • Early visual concept learning with unsupervised deep learning. arXiv preprint arXiv:1606.05579.
  • Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural computation, 18(7), 1527-1554.
  • Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580.
  • Hu, R., Jia, W., Ling, H., & Huang, D. (2012). Multiscale distance matrix for fast plant leaf recognition. IEEE Transactions on Image Processing, 21(11), 4667-4672.
  • Ioffe, S., & Szegedy, C. (2015, June). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning (pp. 448-456).
  • Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).
  • Kulkarni, A. H., Rai, H. M., Jahagirdar, K. A., & Upparamani, P. S. (2013). A leaf recognition technique for plant classification using RBPNN and Zernike moments. International Journal of Advanced Research in Computer and Communication Engineering, 2(1), 984-988.
  • Kulkarni, A. H., Rai, H. M., Jahagirdar, K. A., & Upparamani, P. S. (2013). A leaf recognition technique for plant classification using RBPNN and Zernike moments. International Journal of Advanced Research in Computer and Communication Engineering, 2(1), 984-988.
  • Kumar, N., Belhumeur, P. N., Biswas, A., Jacobs, D. W., Kress, W. J., Lopez, I. C., & Soares, J. V. (2012). Leafsnap: A computer vision system for automatic plant species identification. In Computer vision–ECCV 2012 (pp. 502-516). Springer, Berlin, Heidelberg.
  • Kumar, P. S., Rao, K. N. V., Raju, A. S. N., & Kumar, D. N. (2016, December). Leaf classification based on Shape and Edge feature with k-NN Classifier. In Contemporary Computing and Informatics (IC3I), 2016 2nd International Conference on (pp. 548-552). IEEE.
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436.
  • Lee, C. L., & Chen, S. Y. (2006). Classification of leaf images. International Journal of Imaging Systems and Technology, 16(1), 15-23.
  • Lee, K. B., & Hong, K. S. (2013). An implementation of leaf recognition system using leaf vein and shape. International Journal of Bio-Science and Bio-Technology, 5(2), 57-66.
  • Liu, L., Shen, C., & van den Hengel, A. (2015). The treasure beneath convolutional layers: Cross-convolutional-layer pooling for image classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4749-4757).

The Comparison Of Leaf Classification Performance Of Deep Learning Algorithms

Year 2018, Volume: 1 Issue: 1, 10 - 21, 02.04.2018

Ferdi Doğan , İbrahim Türkoğlu

Abstract

In this article, the achievements of deep learning algorithms have been put forward by classifying plant leaves through deep learning although there are many methods used for the classification of plant leaves. Results are obtained through pre-treatment, feature extraction and classification method in classification processes made with image processing methods. There is no need for such operations in deep learning methods. In deep learning methods, the steps such as pre-processing and feature extraction are performed through Convolution Neural Networks.
In this study, there are about 1900 images of 32 samples in the database used as leaf pattern. There is an average of 60 images for each image class. The images here have been increased 4 times by reflection and reversing operations and processes have been made with approximately 7600 images.
Deep learning algorithms such as AlexNet, Vgg16, Vgg19, ResNet50 and GoogleNet have been used for leaf classification applications for each algorithm and their performance has been evaluated.

Keywords

Deep Learning Convolutional Neural Networks Leaf Classification Deep Learning Algorithm

References

  • Aakif, A., & Khan, M. F. (2015). Automatic classification of plants based on their leaves. Biosystems Engineering, 139, 66-75.
  • Adler, A., Elad, M., & Zibulevsky, M. (2016). Compressed Learning: A Deep Neural Network Approach. arXiv preprint arXiv:1610.09615.
  • Arribas, J. I., Sánchez-Ferrero, G. V., Ruiz-Ruiz, G., & Gómez-Gil, J. (2011). Leaf classification in sunflower crops by computer vision and neural networks. Computers and Electronics in Agriculture, 78(1), 9-18.
  • Bama, B. S., Valli, S. M., Raju, S., & Kumar, V. A. (2011). Content based leaf image retrieval (CBLIR) using shape, color and texture features. Indian Journal of Computer Science and Engineering, 2(2), 202-211.
  • Belhumeur, P. N., Chen, D., Feiner, S., Jacobs, D. W., Kress, W. J., Ling, H., ... & Zhang, L. (2008, October). Searching the world’s herbaria: A system for visual identification of plant species. In European Conference on Computer Vision (pp. 116-129). Springer, Berlin, Heidelberg.
  • Castelluccio, M., Poggi, G., Sansone, C., & Verdoliva, L. (2015). Land use classification in remote sensing images by convolutional neural networks. arXiv preprint arXiv:1508.00092.
  • Chaki, J., Parekh, R., & Bhattacharya, S. (2015). Plant leaf recognition using texture and shape features with neural classifiers. Pattern Recognition Letters, 58, 61-68.
  • Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016). Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. arXiv preprint arXiv:1606.00915.
  • Deng, L., & Yu, D. (2014). Deep learning: methods and applications. Foundations and Trends® in Signal Processing, 7(3–4), 197-387. Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1). Cambridge: MIT press.
  • Gu, X., Du, J. X., & Wang, X. F. (2005, August). Leaf recognition based on the combination of wavelet transform and gaussian interpolation. In International Conference on Intelligent Computing (pp. 253-262). Springer, Berlin, Heidelberg.
  • Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S. (2016). Deep learning for visual understanding: A review. Neurocomputing, 187, 27-48.
  • He, K., Zhang, X., Ren, S., & Sun, J. (2015). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In Proceedings of the IEEE international conference on computer vision (pp. 1026-1034).
  • He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
  • Hebb, D. O. (2005). The organization of behavior: A neuropsychological theory. Psychology Press. Higgins, I., Matthey, L., Glorot, X., Pal, A., Uria, B., Blundell, C., ... & Lerchner, A. (2016).
  • Early visual concept learning with unsupervised deep learning. arXiv preprint arXiv:1606.05579.
  • Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural computation, 18(7), 1527-1554.
  • Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580.
  • Hu, R., Jia, W., Ling, H., & Huang, D. (2012). Multiscale distance matrix for fast plant leaf recognition. IEEE Transactions on Image Processing, 21(11), 4667-4672.
  • Ioffe, S., & Szegedy, C. (2015, June). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning (pp. 448-456).
  • Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).
  • Kulkarni, A. H., Rai, H. M., Jahagirdar, K. A., & Upparamani, P. S. (2013). A leaf recognition technique for plant classification using RBPNN and Zernike moments. International Journal of Advanced Research in Computer and Communication Engineering, 2(1), 984-988.
  • Kulkarni, A. H., Rai, H. M., Jahagirdar, K. A., & Upparamani, P. S. (2013). A leaf recognition technique for plant classification using RBPNN and Zernike moments. International Journal of Advanced Research in Computer and Communication Engineering, 2(1), 984-988.
  • Kumar, N., Belhumeur, P. N., Biswas, A., Jacobs, D. W., Kress, W. J., Lopez, I. C., & Soares, J. V. (2012). Leafsnap: A computer vision system for automatic plant species identification. In Computer vision–ECCV 2012 (pp. 502-516). Springer, Berlin, Heidelberg.
  • Kumar, P. S., Rao, K. N. V., Raju, A. S. N., & Kumar, D. N. (2016, December). Leaf classification based on Shape and Edge feature with k-NN Classifier. In Contemporary Computing and Informatics (IC3I), 2016 2nd International Conference on (pp. 548-552). IEEE.
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436.
  • Lee, C. L., & Chen, S. Y. (2006). Classification of leaf images. International Journal of Imaging Systems and Technology, 16(1), 15-23.
  • Lee, K. B., & Hong, K. S. (2013). An implementation of leaf recognition system using leaf vein and shape. International Journal of Bio-Science and Bio-Technology, 5(2), 57-66.
  • Liu, L., Shen, C., & van den Hengel, A. (2015). The treasure beneath convolutional layers: Cross-convolutional-layer pooling for image classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4749-4757).
There are 28 citations in total.

Details

Primary Language Turkish
Subjects Computer Software
Journal Section Articles
Authors

Ferdi Doğan

İbrahim Türkoğlu

Publication Date April 2, 2018
Submission Date February 28, 2018
Acceptance Date March 20, 2018
Published in Issue Year 2018Volume: 1 Issue: 1

Cite

IEEE F. Doğan and İ. Türkoğlu, “Derin Öğrenme Algoritmalarının Yaprak Sınıflandırma Başarımlarının Karşılaştırılması”, SAUCIS, vol. 1, no. 1, pp. 10–21, 2018.
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