Year 2015,
Volume: 3 Issue: 4, 136 - 139, 15.12.2015
Abstract
A number of shape features for automatic plant recognition based on digital image processing have been proposed by Pauwels et al. in 2009. Then Silva et al in 2014 have presented database comprises 40 different plant species. We performed in our study a classification process using dataset and artificial neural networks which have been prepared by Silva and et al. It has been determined that classification accuracy is over 92%.
References
- “Evaluation of Features for Leaf Discrimination”, Pedro F. B. Silva, Andre R.S. Marcal,Rubim M. Almeida da Silva (2013), Springer Lecture Notes in Computer Science, Vol. 7950, 197-204.
- “Development of a System for Automatic Plant Species Recognition”, Pedro Filipe Silva,Disserta¸ca˜o de Mestrado (Master’s Thesis), Faculdade de Ciˆencias da Universidade do Porto. Available for download or online reading at http://hdl.handle.net/10216/67734
- Haykin, S., Neural networks a comprehensive foundation, 1994.
- Ertunç, H.M., Ocak, H., Aliustaoğlu, C., "ANN and ANFIS based multi-staged decision algorithm for the detection and diagnosis of bearing faults", Neural Comput and Application, 2012.
- Uğur A., KINACI A.C., “Yapay Zeka Teknikleri ve Yapay Sinir Ağları Kullanılarak Web Sayfalarının Sınıflandırılması”, 11.İnternet Konferansları, 2006. (in Turkish)
- Çelik E., Atalay M., Bayer H., “Yapay Sinir Ağlari Ve Destek Vektör Makineleri İle Deprem Tahmininde Sismik Darbelerin Kullanilmasi Earthquake Prediction Using Seismic Bumps With Artificial Neural Networks And Support Vector Machines”,2014.
- Özkan Ö., Yildiz M., Köklükaya E., “Fibromiyalji Sendromunun Teşhisinde Kullanilan Laboratuar Testlerinin Sempatik Deri Cevabi Parametreleriyle Desteklenerek Teşhis Doğruluğunun Arttirilmasi”2011. (in Turkish)
- Sağıroğlu, Ş., Beşdok, E., Erler, M., Mühendislikte yapay zeka uygulamaları-I, Ufuk Kitap Kırtasiye-Yayıncılık Tic. Ltd., 2003. (in Turkish)
- The MathWorks, Inc., MATLAB® Documentation Neural Network Toolbox Help, “Levenberg-Marquardt Algorithm”, Release 2009a, 2009.
Year 2015,
Volume: 3 Issue: 4, 136 - 139, 15.12.2015
Abstract
References
- “Evaluation of Features for Leaf Discrimination”, Pedro F. B. Silva, Andre R.S. Marcal,Rubim M. Almeida da Silva (2013), Springer Lecture Notes in Computer Science, Vol. 7950, 197-204.
- “Development of a System for Automatic Plant Species Recognition”, Pedro Filipe Silva,Disserta¸ca˜o de Mestrado (Master’s Thesis), Faculdade de Ciˆencias da Universidade do Porto. Available for download or online reading at http://hdl.handle.net/10216/67734
- Haykin, S., Neural networks a comprehensive foundation, 1994.
- Ertunç, H.M., Ocak, H., Aliustaoğlu, C., "ANN and ANFIS based multi-staged decision algorithm for the detection and diagnosis of bearing faults", Neural Comput and Application, 2012.
- Uğur A., KINACI A.C., “Yapay Zeka Teknikleri ve Yapay Sinir Ağları Kullanılarak Web Sayfalarının Sınıflandırılması”, 11.İnternet Konferansları, 2006. (in Turkish)
- Çelik E., Atalay M., Bayer H., “Yapay Sinir Ağlari Ve Destek Vektör Makineleri İle Deprem Tahmininde Sismik Darbelerin Kullanilmasi Earthquake Prediction Using Seismic Bumps With Artificial Neural Networks And Support Vector Machines”,2014.
- Özkan Ö., Yildiz M., Köklükaya E., “Fibromiyalji Sendromunun Teşhisinde Kullanilan Laboratuar Testlerinin Sempatik Deri Cevabi Parametreleriyle Desteklenerek Teşhis Doğruluğunun Arttirilmasi”2011. (in Turkish)
- Sağıroğlu, Ş., Beşdok, E., Erler, M., Mühendislikte yapay zeka uygulamaları-I, Ufuk Kitap Kırtasiye-Yayıncılık Tic. Ltd., 2003. (in Turkish)
- The MathWorks, Inc., MATLAB® Documentation Neural Network Toolbox Help, “Levenberg-Marquardt Algorithm”, Release 2009a, 2009.
There are 9 citations in total.
Details
| Journal Section | Research Article |
|---|---|
| Authors | |
| Publication Date | December 15, 2015 |
| Published in Issue | Year 2015 Volume: 3 Issue: 4 |