Research Article
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MODERN LEARNING TECHNIQUES AND PLANT IMAGE CLASSIFICATION

Year 2020, Volume: 62 Issue: 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

Abstract

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. 

Keywords

Plan Classification Convolutional Neural Network AlexNet VGGNet

References

  • 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.

Year 2020, Volume: 62 Issue: 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

Abstract

References

  • 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.
There are 19 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

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

Publication Date December 31, 2020
Submission Date October 1, 2019
Acceptance Date August 26, 2020
Published in Issue Year 2020 Volume: 62 Issue: 2

Cite

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. December 2020;62(2):153-163. doi:10.33769/aupse.627897
Chicago Ünal, Metehan, Erkan Bostanci, Mehmet Serdar Guzel, and Ayhan Aydın. “MODERN LEARNING TECHNIQUES AND PLANT IMAGE CLASSIFICATION”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 62, no. 2 (December 2020): 153-63. https://doi.org/10.33769/aupse.627897.
EndNote Ünal M, Bostanci E, Guzel MS, Aydın A (December 1, 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, and A. Aydın, “MODERN LEARNING TECHNIQUES AND PLANT IMAGE CLASSIFICATION”, Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng., vol. 62, no. 2, pp. 153–163, 2020, doi: 10.33769/aupse.627897.
ISNAD Ünal, Metehan et al. “MODERN LEARNING TECHNIQUES AND PLANT IMAGE CLASSIFICATION”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 62/2 (December 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 et al. “MODERN LEARNING TECHNIQUES AND PLANT IMAGE CLASSIFICATION”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, vol. 62, no. 2, 2020, pp. 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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