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ZEYTİN YAPRAĞINDAKİ HASTALIKLARIN SINIFLANDIRILMASINDA ÖN EĞİTİMLİ EVRİŞİMLİ SİNİR AĞLARININ PERFORMANSLARININ İNCELENMESİ

Year 2022, Volume: 10 Issue: 3, 535 - 547, 01.09.2022
https://doi.org/10.36306/konjes.1078358

Abstract

Zeytin ülkemizin belirli bölgelerinde yetişen oldukça önemli bir üründür. Gümrük ve Ticaret Bakanlığı’nın verilerine göre 2019 yılında yaklaşık 420 bin ton sofralık zeytin üretimi ile dünyadaki toplam üretimin %14’ten fazlası ülkemizde yapılmıştır. Böylece, zeytin yaprağındaki hastalıkların erken teşhisi ve tedavisi üretim kapasitesinin artmasına yol açabilir. Günümüzde birçok alanda olduğu gibi bitki hastalıklarının teşhisi için derin öğrenme algoritmaları yaygın olarak kullanılmaktadır. Bu çalışmada, AlexNet, SqueezeNet, ShuffleNet ve GoogleNet gibi sıklıkla tercih edilen ön eğitimli derin öğrenme ağları ile zeytin yaprağındaki hastalıkların sınıflandırılması gerçekleştirilmiştir. Ağ yapıları, zeytin yaprağındaki hastalıkların etiketlerine göre eğitim için yeniden düzenlenmiştir. Veri setinde, veri çoğaltma işlemi uygulanarak hem ham veri seti hem de çoğaltılmış veri seti için ayrı ayrı performans sonuçları alınmıştır. Elde edilen sonuçlar doğruluk, duyarlılık, özgüllük, kesinlik ve F1-Skor gibi performans ölçütleri ile değerlendirilmiştir. En iyi performans iyileştirmesi %7,56 ile AlexNet’in doğruluk değeri için elde edilirken, en düşük iyileştirme oranı %0,63 ile ShuffleNet’in özgüllük değerinden elde edilmiştir.

References

  • Albawi S., Mohammed T. A., Al-Zawi S., 2017,” Understanding of a convolutional neural network”, International Conference on Engineering and Technology (ICET), ss. 1-6.
  • Bloice M. D., Christof S., Andreas H., 2017,” Augmentor: an image augmentation library for machine learning”, çevrimiçi, https://arxiv.org/abs/1708.04680.
  • Buda M., Maki A., Mazurowski M. A., 2018, “A systematic study of the class imbalance problem in convolutional neural networks”, Neural Networks, Cilt 106, ss. 249-259.
  • Darwish A., Ezzat D., Hassanien A. E., 2020,“An optimized model based on convolutional neural networks and orthogonal learning particle swarm optimization algorithm for plant diseases diagnosis”, Swarm and evolutionary computation, Cilt 52, Sayı 100616, ss. 1-12.
  • Deepak S., Ameer, P. M., 2019., “Brain tumor classification using deep CNN features via transfer learning”, Computers in biology and medicine, Cilt 111, ss. Sayı 103345, ss. 1-7.
  • Deng J., Dong W., Socher R., Li L. J., Li K., Fei-Fei L.,2009,” ImageNet: A large-scale hierarchical image database”, 2009 IEEE Conference on Computer Vision and Pattern Recognition, ss. 248-255.
  • Erilmez S., Erkan S., 2014, “The identification of virus diseases in olive trees in Aydin, Balikesir and İzmir provinces and the determination of their present status” Plant Protection Bulletin, Cilt 54, Sayı 1, ss. 45-67.
  • Floridi L., 2020, “AI and Its New Winter: from Myths to Realities”, Philosophy & Technology, Cilt 33, ss. 1-3. GeethaRamani R., ArunPandian J., 2019, “Identification of plant leaf diseases using a nine-layer deep convolutional neural network”, Comput. Electr. Eng., Cilt 76, ss. 323-338.
  • Iandola F. N., Han S., Moskewicz M. W., Ashraf K., Dally W. J., Keutzer K., 2016, “SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size”, International Conference on Learning Representations (ICLR),çevrimiçi, https://arxiv.org/abs/1602.07360, ss. 1-13.
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  • Korkut U. B., Göktürk Ö. B., Yıldız O., 2018, “Detection of plant diseases by machine learning” 26th Signal Processing and Communications Applications Conference (SIU), ss. 1-4.
  • Krizhevsky A., Sutskever I., Hinton, G. E., 2012, ”Imagenet classification with deep convolutional neural networks” Advances in neural information processing systems, Cilt 25, ss. 1-9.
  • Liu B., Zhang Y., He D., Li Y., 2018, “Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks” Symmetry , Cilt 10, Sayı 11, ss. 1-16.
  • Mohanty P. S., Hughes P. D., Salathé M., 2016, “Using deep learning forımage-based plant disease detection”, Frontiers in Plant Science, Cilt 7, Sayı 1419, ss. 1-10.
  • O’Shea K., Nash R., 2015, “An introduction to convolutional neural networks”,çevrimiçi, https://arxiv.org/abs/1511.08458, ss. 1-11.
  • Pawar P., Turkar V., Patil P.,2016,” Cucumber disease detection using artificial neural network”, International Conference on Inventive Computation Technologies (ICICT), Cilt 3, ss. 1-5.
  • Shorten C., Khoshgoftaar T. M., 2019, ” Shorten, Connor, and Taghi M. Khoshgoftaar. "A survey on image data augmentation for deep learning”, Journal of big data, Cilt 6, Sayı 1, ss. 1-48.
  • Szegedy C., Liu W. Jia Y., Sermanet P.,Reed S., Anguelov D., Erhan D., Vanhoucke V., Rabinovich A., 2015,” Going Deeper With Convolutions”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), ss. 1-9.
  • Türkoğlu M. , Hanbay K. , Saraç Sivrikaya I. , Hanbay D., 2020, “Derin Evrişimsel Sinir Ağı Kullanılarak Kayısı Hastalıklarının Sınıflandırılması”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, Cilt 9, Sayı 1, ss. 334-345.
  • Uğuz S., CNN_olive_Dataset, https://github.com/sinanuguz/CNN_olive_dataset, ziyaret tarihi: 23.02.2022. Uğuz S., Uysal N., 2021,“Classification of olive leaf diseases using deep convolutional neural networks”, Neural Comput & Applic, Cilt 33 Sayı 9, ss. 4133–4149.
  • Upadhyay S. K., Kumar A., 2021, ”Early-stage brown spot disease recognition in paddy using ımage processing and deep learning techniques”, International Information and Engineering Technology Association, Cilt 38, Sayı 6, ss. 1755-1766.
  • Uysal N., 2020 Zeytin yaprağındaki hastalıkların derin öğrenme teknikleri kullanılarak sınıflandırılması, Yüksek Lisans Tezi, Isparta Uygulamalı Bilimler Üniversitesi, Lisansüstü Eğitim Enstitüsü, Isparta.
  • Zhang X., Zhou X., Lin M. Sun J.,2018,” Shufflenet: an extremely efficient convolutional neural network for mobile devices” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), ss. 6848-6856.

Performance Investigation of Pre-Trained Convolutional Neural Networks in Olive Leaf Disease Classification

Year 2022, Volume: 10 Issue: 3, 535 - 547, 01.09.2022
https://doi.org/10.36306/konjes.1078358

Abstract

Olive is a very significant crop grown in specific regions of our country. According to the data of the Ministry of Customs and Trade, with the production of approximately 420 thousand tons of table olives in 2019, more than 14% of the total production in the world was made in Turkey. Therefore, early diagnosis and treatment of diseases in olive leaves can lead to increased production capacity. Today, as in many fields, deep learning algorithms are widely used for the diagnosis of plant diseases. In this study, the classification of olive leaf diseases was carried out with the frequently preferred pre-trained deep learning networks such as AlexNet, SqueezeNet, ShuffleNet, and GoogleNet. In the data set, performance results were obtained for both the raw data set and the augmented data set by applying the data augmentation process. The obtained results were evaluated with the performance criteria as accuracy, sensitivity, specificity, precision, and F1-Score. While the best performance improvement was obtained for the accuracy value of AlexNet with 7.56%, the lowest improvement rate was obtained from the specificity value of ShuffleNet with 0.63%.

References

  • Albawi S., Mohammed T. A., Al-Zawi S., 2017,” Understanding of a convolutional neural network”, International Conference on Engineering and Technology (ICET), ss. 1-6.
  • Bloice M. D., Christof S., Andreas H., 2017,” Augmentor: an image augmentation library for machine learning”, çevrimiçi, https://arxiv.org/abs/1708.04680.
  • Buda M., Maki A., Mazurowski M. A., 2018, “A systematic study of the class imbalance problem in convolutional neural networks”, Neural Networks, Cilt 106, ss. 249-259.
  • Darwish A., Ezzat D., Hassanien A. E., 2020,“An optimized model based on convolutional neural networks and orthogonal learning particle swarm optimization algorithm for plant diseases diagnosis”, Swarm and evolutionary computation, Cilt 52, Sayı 100616, ss. 1-12.
  • Deepak S., Ameer, P. M., 2019., “Brain tumor classification using deep CNN features via transfer learning”, Computers in biology and medicine, Cilt 111, ss. Sayı 103345, ss. 1-7.
  • Deng J., Dong W., Socher R., Li L. J., Li K., Fei-Fei L.,2009,” ImageNet: A large-scale hierarchical image database”, 2009 IEEE Conference on Computer Vision and Pattern Recognition, ss. 248-255.
  • Erilmez S., Erkan S., 2014, “The identification of virus diseases in olive trees in Aydin, Balikesir and İzmir provinces and the determination of their present status” Plant Protection Bulletin, Cilt 54, Sayı 1, ss. 45-67.
  • Floridi L., 2020, “AI and Its New Winter: from Myths to Realities”, Philosophy & Technology, Cilt 33, ss. 1-3. GeethaRamani R., ArunPandian J., 2019, “Identification of plant leaf diseases using a nine-layer deep convolutional neural network”, Comput. Electr. Eng., Cilt 76, ss. 323-338.
  • Iandola F. N., Han S., Moskewicz M. W., Ashraf K., Dally W. J., Keutzer K., 2016, “SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size”, International Conference on Learning Representations (ICLR),çevrimiçi, https://arxiv.org/abs/1602.07360, ss. 1-13.
  • Jadhav S. B., Udupi V. R., Patil S. B., 2021, ” Identification of plant diseases using convolutional neural networks”, Int. j. inf. tecnol, Cilt 13, ss. 2461–2470.
  • Korkut U. B., Göktürk Ö. B., Yıldız O., 2018, “Detection of plant diseases by machine learning” 26th Signal Processing and Communications Applications Conference (SIU), ss. 1-4.
  • Krizhevsky A., Sutskever I., Hinton, G. E., 2012, ”Imagenet classification with deep convolutional neural networks” Advances in neural information processing systems, Cilt 25, ss. 1-9.
  • Liu B., Zhang Y., He D., Li Y., 2018, “Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks” Symmetry , Cilt 10, Sayı 11, ss. 1-16.
  • Mohanty P. S., Hughes P. D., Salathé M., 2016, “Using deep learning forımage-based plant disease detection”, Frontiers in Plant Science, Cilt 7, Sayı 1419, ss. 1-10.
  • O’Shea K., Nash R., 2015, “An introduction to convolutional neural networks”,çevrimiçi, https://arxiv.org/abs/1511.08458, ss. 1-11.
  • Pawar P., Turkar V., Patil P.,2016,” Cucumber disease detection using artificial neural network”, International Conference on Inventive Computation Technologies (ICICT), Cilt 3, ss. 1-5.
  • Shorten C., Khoshgoftaar T. M., 2019, ” Shorten, Connor, and Taghi M. Khoshgoftaar. "A survey on image data augmentation for deep learning”, Journal of big data, Cilt 6, Sayı 1, ss. 1-48.
  • Szegedy C., Liu W. Jia Y., Sermanet P.,Reed S., Anguelov D., Erhan D., Vanhoucke V., Rabinovich A., 2015,” Going Deeper With Convolutions”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), ss. 1-9.
  • Türkoğlu M. , Hanbay K. , Saraç Sivrikaya I. , Hanbay D., 2020, “Derin Evrişimsel Sinir Ağı Kullanılarak Kayısı Hastalıklarının Sınıflandırılması”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, Cilt 9, Sayı 1, ss. 334-345.
  • Uğuz S., CNN_olive_Dataset, https://github.com/sinanuguz/CNN_olive_dataset, ziyaret tarihi: 23.02.2022. Uğuz S., Uysal N., 2021,“Classification of olive leaf diseases using deep convolutional neural networks”, Neural Comput & Applic, Cilt 33 Sayı 9, ss. 4133–4149.
  • Upadhyay S. K., Kumar A., 2021, ”Early-stage brown spot disease recognition in paddy using ımage processing and deep learning techniques”, International Information and Engineering Technology Association, Cilt 38, Sayı 6, ss. 1755-1766.
  • Uysal N., 2020 Zeytin yaprağındaki hastalıkların derin öğrenme teknikleri kullanılarak sınıflandırılması, Yüksek Lisans Tezi, Isparta Uygulamalı Bilimler Üniversitesi, Lisansüstü Eğitim Enstitüsü, Isparta.
  • Zhang X., Zhou X., Lin M. Sun J.,2018,” Shufflenet: an extremely efficient convolutional neural network for mobile devices” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), ss. 6848-6856.
There are 23 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Research Article
Authors

Bünyamin Dikici 0000-0001-6722-5894

Mehmet Fatih Bekçioğulları 0000-0002-0056-9526

Hakan Açıkgöz 0000-0002-6432-7243

Deniz Korkmaz 0000-0002-5159-0659

Publication Date September 1, 2022
Submission Date February 24, 2022
Acceptance Date June 7, 2022
Published in Issue Year 2022 Volume: 10 Issue: 3

Cite

IEEE B. Dikici, M. F. Bekçioğulları, H. Açıkgöz, and D. Korkmaz, “ZEYTİN YAPRAĞINDAKİ HASTALIKLARIN SINIFLANDIRILMASINDA ÖN EĞİTİMLİ EVRİŞİMLİ SİNİR AĞLARININ PERFORMANSLARININ İNCELENMESİ”, KONJES, vol. 10, no. 3, pp. 535–547, 2022, doi: 10.36306/konjes.1078358.