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MODERN LEARNING TECHNIQUES AND PLANT IMAGE CLASSIFICATION

Yıl 2020, Cilt: 62 Sayı: 2, 153 - 163, 31.12.2020

Metehan Ünal Erkan Bostanci Mehmet Serdar Guzel Ayhan Aydın

https://doi.org/10.33769/aupse.627897
Cited By: 1

Öz

The intelligent machines concept is born in sci-fi
scenarios. Today it seems to be we are much closer to realizing this idea than
ever before. By imitating the human nervous system, machines can learn many
things. This paper explains modern learning techniques like artificial neural
networks, transfer learning. Later purposes an experiment to classify plant
seedling images to test the transfer learning with two different CNN
architectures. Although the architects were not actually created for this task,
result were quite accurate for a different classification task. 

Anahtar Kelimeler

Plan Classification Convolutional Neural Network AlexNet VGGNet

Kaynakça

  • Turing, A.M., Computing Machinery And Intelligence, Mind, Volume LIX, Issue 236, 1 October 1950, 433-460, https://doi.org/10.1093/mind/LIX.236.433.
  • Russell, S. J., Norvig, P. Artificial intelligence: a modern approach, Malaysia, Pearson Education Limited, 2016.
  • Zhang, L., Zhang, B. A geometrical representation of McCulloch-Pitts neural model and its applications, IEEE Transactions on Neural Networks, 10(4) (1999). 925-929.
  • Anonymous, Web Site: http://cs231n.github.io/convolutional-networks/, Access Date: 26.02.2017
  • Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., Fei-Fei, L., Imagenet: A large-scale hierarchical image database, IEEE conference on computer vision and pattern recognition, IEEE, (2009, June), (pp. 248-255).
  • Krizhevsky, Alex, Sutskever, I., Geoffrey, E.H., Imagenet classification with deep convolutional neural networks, Advances in neural information processing systems, (2012).
  • Simonyan, K., Zisserman, A., Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556 (2014).
  • Yalcin, H., Razavi, S., Plant classification using convolutional neural networks, Fifth International Conference on Agro-Geoinformatics (Agro-Geoinformatics), IEEE, (2016, July), 1-5.
  • Chaki, J., Parekh, R., Designing an automated system for plant leaf recognition, International Journal of Advances in Engineering & Technology, 2(1) (2012), 149.
  • Gwo, C. Y., Wei, C. H., Li, Y., Rotary matching of edge features for leaf recognition, Computers and electronics in agriculture, 91 (2013), 124-134.
  • Arribas, J. I., Sánchez-Ferrero, G. V., Ruiz-Ruiz, G., Gómez-Gil, J., Leaf classification in sunflower crops by computer vision and neural networks, Computers and Electronics in Agriculture, 78(1) (2011), 9-18.
  • Husin, Z., Shakaff, A. Y. M., Aziz, A. H. A., Farook, R. S. M., Jaafar, M. N., Hashim, U., Harun, A., Embedded portable device for herb leaves recognition using image processing techniques and neural network algorithm, Computers and Electronics in Agriculture, 89 (2012), 18-29.
  • Pydipati, R., Burks, T. F., Lee, W. S., Identification of citrus disease using color texture features and discriminant analysis, Computers and Electronics in Agriculture, 52(1-2) (2006), 49-59.
  • Scoffoni, C., Rawls, M., McKown, A., Cochard, H., Sack, L., Decline of leaf hydraulic conductance with dehydration: relationship to leaf size and venation architecture, Plant Physiology, (2011), 111.
  • Larese, M. G., Baya, A. E., Craviotto, R. M., Arango, M. R., Gallo, C., & Granitto, P. M., Multiscale recognition of legume varieties based on leaf venation images, Expert Systems with Applications, 41(10) (2014), 4638-4647.
  • Grinblat, G. L., Uzal, L. C., Larese, M. G., Granitto, P. M., Deep learning for plant identification using vein morphological patterns, Computers and Electronics in Agriculture, (127) (2016), 418-424.
  • Lee, S. H., Chan, C. S., Wilkin, P., Remagnino, P., Deep-plant: Plant identification with convolutional neural networks, IEEE International Conference on Image Processing (ICIP), IEEE, (2015, September), 452-456.
  • Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2014, January). Decaf: A deep convolutional activation feature for generic visual recognition. In International conference on machine learning (pp. 647-655).
  • Giselsson, T. M., Jørgensen, R. N., Jensen, P. K., Dyrmann, M., Midtiby, H. S., A public image database for benchmark of plant seedling classification algorithms, (2017), arXiv preprint arXiv:1711.05458.

Yıl 2020, Cilt: 62 Sayı: 2, 153 - 163, 31.12.2020

Metehan Ünal Erkan Bostanci Mehmet Serdar Guzel Ayhan Aydın

https://doi.org/10.33769/aupse.627897
Cited By: 1

Öz

Kaynakça

  • Turing, A.M., Computing Machinery And Intelligence, Mind, Volume LIX, Issue 236, 1 October 1950, 433-460, https://doi.org/10.1093/mind/LIX.236.433.
  • Russell, S. J., Norvig, P. Artificial intelligence: a modern approach, Malaysia, Pearson Education Limited, 2016.
  • Zhang, L., Zhang, B. A geometrical representation of McCulloch-Pitts neural model and its applications, IEEE Transactions on Neural Networks, 10(4) (1999). 925-929.
  • Anonymous, Web Site: http://cs231n.github.io/convolutional-networks/, Access Date: 26.02.2017
  • Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., Fei-Fei, L., Imagenet: A large-scale hierarchical image database, IEEE conference on computer vision and pattern recognition, IEEE, (2009, June), (pp. 248-255).
  • Krizhevsky, Alex, Sutskever, I., Geoffrey, E.H., Imagenet classification with deep convolutional neural networks, Advances in neural information processing systems, (2012).
  • Simonyan, K., Zisserman, A., Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556 (2014).
  • Yalcin, H., Razavi, S., Plant classification using convolutional neural networks, Fifth International Conference on Agro-Geoinformatics (Agro-Geoinformatics), IEEE, (2016, July), 1-5.
  • Chaki, J., Parekh, R., Designing an automated system for plant leaf recognition, International Journal of Advances in Engineering & Technology, 2(1) (2012), 149.
  • Gwo, C. Y., Wei, C. H., Li, Y., Rotary matching of edge features for leaf recognition, Computers and electronics in agriculture, 91 (2013), 124-134.
  • Arribas, J. I., Sánchez-Ferrero, G. V., Ruiz-Ruiz, G., Gómez-Gil, J., Leaf classification in sunflower crops by computer vision and neural networks, Computers and Electronics in Agriculture, 78(1) (2011), 9-18.
  • Husin, Z., Shakaff, A. Y. M., Aziz, A. H. A., Farook, R. S. M., Jaafar, M. N., Hashim, U., Harun, A., Embedded portable device for herb leaves recognition using image processing techniques and neural network algorithm, Computers and Electronics in Agriculture, 89 (2012), 18-29.
  • Pydipati, R., Burks, T. F., Lee, W. S., Identification of citrus disease using color texture features and discriminant analysis, Computers and Electronics in Agriculture, 52(1-2) (2006), 49-59.
  • Scoffoni, C., Rawls, M., McKown, A., Cochard, H., Sack, L., Decline of leaf hydraulic conductance with dehydration: relationship to leaf size and venation architecture, Plant Physiology, (2011), 111.
  • Larese, M. G., Baya, A. E., Craviotto, R. M., Arango, M. R., Gallo, C., & Granitto, P. M., Multiscale recognition of legume varieties based on leaf venation images, Expert Systems with Applications, 41(10) (2014), 4638-4647.
  • Grinblat, G. L., Uzal, L. C., Larese, M. G., Granitto, P. M., Deep learning for plant identification using vein morphological patterns, Computers and Electronics in Agriculture, (127) (2016), 418-424.
  • Lee, S. H., Chan, C. S., Wilkin, P., Remagnino, P., Deep-plant: Plant identification with convolutional neural networks, IEEE International Conference on Image Processing (ICIP), IEEE, (2015, September), 452-456.
  • Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2014, January). Decaf: A deep convolutional activation feature for generic visual recognition. In International conference on machine learning (pp. 647-655).
  • Giselsson, T. M., Jørgensen, R. N., Jensen, P. K., Dyrmann, M., Midtiby, H. S., A public image database for benchmark of plant seedling classification algorithms, (2017), arXiv preprint arXiv:1711.05458.
Toplam 19 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Research Article
Yazarlar

Metehan Ünal 0000-0002-7545-2445

Erkan Bostanci 0000-0001-8547-7569

Mehmet Serdar Guzel 0000-0002-3408-0083

Ayhan Aydın 0000-0001-7938-0509

Yayımlanma Tarihi 31 Aralık 2020
Gönderilme Tarihi 1 Ekim 2019
Kabul Tarihi 26 Ağustos 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 62 Sayı: 2

Kaynak Göster

APA Ünal, M., Bostanci, E., Guzel, M. S., Aydın, A. (2020). MODERN LEARNING TECHNIQUES AND PLANT IMAGE CLASSIFICATION. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, 62(2), 153-163. https://doi.org/10.33769/aupse.627897
AMA Ünal M, Bostanci E, Guzel MS, Aydın A. MODERN LEARNING TECHNIQUES AND PLANT IMAGE CLASSIFICATION. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. Aralık 2020;62(2):153-163. doi:10.33769/aupse.627897
Chicago Ünal, Metehan, Erkan Bostanci, Mehmet Serdar Guzel, ve Ayhan Aydın. “MODERN LEARNING TECHNIQUES AND PLANT IMAGE CLASSIFICATION”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 62, sy. 2 (Aralık 2020): 153-63. https://doi.org/10.33769/aupse.627897.
EndNote Ünal M, Bostanci E, Guzel MS, Aydın A (01 Aralık 2020) MODERN LEARNING TECHNIQUES AND PLANT IMAGE CLASSIFICATION. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 62 2 153–163.
IEEE M. Ünal, E. Bostanci, M. S. Guzel, ve A. Aydın, “MODERN LEARNING TECHNIQUES AND PLANT IMAGE CLASSIFICATION”, Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng., c. 62, sy. 2, ss. 153–163, 2020, doi: 10.33769/aupse.627897.
ISNAD Ünal, Metehan vd. “MODERN LEARNING TECHNIQUES AND PLANT IMAGE CLASSIFICATION”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 62/2 (Aralık 2020), 153-163. https://doi.org/10.33769/aupse.627897.
JAMA Ünal M, Bostanci E, Guzel MS, Aydın A. MODERN LEARNING TECHNIQUES AND PLANT IMAGE CLASSIFICATION. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. 2020;62:153–163.
MLA Ünal, Metehan vd. “MODERN LEARNING TECHNIQUES AND PLANT IMAGE CLASSIFICATION”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, c. 62, sy. 2, 2020, ss. 153-6, doi:10.33769/aupse.627897.
Vancouver Ünal M, Bostanci E, Guzel MS, Aydın A. MODERN LEARNING TECHNIQUES AND PLANT IMAGE CLASSIFICATION. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. 2020;62(2):153-6.

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