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Evrişimli Sinir Ağı ile Çeltik Bitkisi Hastalığının Tespiti

Year 2022, Volume: 11 Issue: 1, 203 - 217, 24.03.2022
https://doi.org/10.17798/bitlisfen.1014393

Abstract

Dünya çapında pirinç tüketimi göz önüne alındığında pirincin önemli bir yere sahip olduğu görülür. Çeltik bitkisi, buğdaygiller ailesinden mısır ve buğdaydan sonra en fazla ekimi yapılan bitkidir. Tarım alanındaki son araştırma konularından birisi de, bir bitkinin yaprak görüntülerinden hastalıkların tanınması veya sınıflandırılmasıdır. Yaprak görüntülerinden çeltik hastalığının otomatik bir şekilde teşhisi geliştirme aşamasında olan bir araştırma konusudur. Bu gelişime katkı sağlamak için farklı öğrenme yöntemleri kullanılarak hastalığın erken teşhisi için önemli çalışmalar yapılmaktadır. Önceki çalışmalarda, hastalıkları tespit etmek için bitki yaprak görüntüleri üzerinde standart öznitelik çıkarma yöntemleri kullanılmıştır. Bu çalışmada ise temel olarak hastalıkları tespit etmek için bir makine öğrenme yöntemi olan derin öğrenme modelleri kullanılmıştır. Bu çalışmada derin Evrişimli Sinir Ağı (ESA) kullanılarak çeltik bitkisinin hastalıklı olup olmadığı tespit edilmiştir. Çalışmada kullanılan 5000 adet çeltik bitkisi yaprağına ait veri seti Kaggle sitesinden alınmıştır. Hastalığın tespiti için çeltik bitkisine ait üç hastalık (BrowSpot, LeafBlast ve Hispa) ve sağlıklı olmak üzere toplam iki çeşit sınıflandırma yapılmıştır. Çeltik bitkisinin hastalığının tespiti için kullanılan ESA'nın hiper-parametrelerinde değişiklik yapılarak %91,54’lük bir başarı oranı elde edilmiştir. Veri artırma yöntemiyle veri setindeki 5000 görüntüden 8000 çeltik bitki yaprağı görüntüsü elde edilmiş ve ESA için bu görüntüler üzerinden yapılan eğitimden sonra %94,87’lik bir başarı oranı elde edilmiştir. Kullanılan veri setindeki görüntüler üzerinden ön işlem yapıldıktan sonra ESA ile eğitim işleminden sonra %97,57’lik bir başarı oranı elde edilmiştir. Çeltik bitkisi yaprak görüntülerinden hastalık tespiti için elde edilen başarı sonucu, yöntemin uygulanabilirliğini göstermektedir.

References

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Year 2022, Volume: 11 Issue: 1, 203 - 217, 24.03.2022
https://doi.org/10.17798/bitlisfen.1014393

Abstract

References

  • [1] Electrical and Electronics Engineers 2015; 7(1): 239-250. Weizheng S, Yachun W, Zhanliang C, Hongda W. Grading method of leaf spot disease based on image processing. In Computer Science and Software Engineering, 2008 International Conference on IEEE 2008 Dec 12; 6: 491-494.
  • [2] Rice Production (Peace Corps): Chapter 14 – Diseases of rice. Last accessed on 6 December 2020. url: http://www.nzdl.
  • [3] Karmokar, B. C., Ullah, M. S., Siddiquee, M. K., & Alam, K. M. R. (2015). Tea leaf diseases recognition using neural network ensemble. International Journal of Computer Application, 114(17).
  • [4] Wang, G., Sun, Y., & Wang, J. (2017). Automatic image-based plant disease severity estimation using deep learning. Computational Intelligence and Neuroscience.
  • [5] Fuentes, A., Yoon, S., Kim, S. C., & Park, D. S. (2017). A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors, 17(9), 2022.
  • [6] Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., & Stefanovic, D. (2016). Deep neural networks based recognition of plant diseases by leaf image classification. Computational Intelligence and Neuroscience.
  • [7] Jiang Lu, Jie Hu, Guannan Zhao, Fenghua Mei, Changshui Zhang, An in-field automatic wheat disease diagnosis system, Computers and Electronics in Agriculture, Volume 142, Part A, 2017, Pages 369-379.
  • [8] Bhagawati, R., Bhagawati, K., Singh, A., Nongthombam, R., Sarmah, R., & Bhagawati, G. (2015). Artificial neural network assisted weather based plant disease forecasting system. International Journal on Recent and Innovation Trends in Computing and Communication, 3(6), 4168e4173.
  • [9] Mohanty, S. P., Hughes, D. P., & Salath_e, M. (2016). Using deep learning for image-based plant disease detection. Frontiers of Plant Science, 7.
  • [10] Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., & Stefanovic, D. (2016). Deep neural networks based recognition of plant diseases by leaf image classification. Computational Intelligence and Neuroscience.
  • [11] Krizhevsky, A., Sutskever, I., Hinton, G.E., 2012. Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems (NIPS), pp. 1097-1105.
  • [12] Simonyan, K., Zisserman, A., 2015. Very deep convolutional networks for large-scale image recognition. International Conference on Learning Representations (ICLR).
  • [13] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z., 2016. Rethinking the inception architecture for computer vision. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2818-2826.
  • [14] He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep residual learning for image recognition. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770-778.
  • [15] Kerkech, M., Hafiane, A., Canals, R., 2018. Deep leaning approach with colorimetric spaces and vegetation indices for vine diseases detection in UAV images. Computers and Electronics in Agriculture Dec 2018, Volume 155, 237-243.
  • [16] Ma, J., Du, K., Zheng, F., Zhang, L., Gong, Z., Sun, Z., 2018. A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network. Computers and Electronics in Agriculture Nov 2018, Volume 154, 18-24.
  • [17] Lua, Y., Yi, S., Zeng, N., Liu, Y., Zhang, Y., 2017. Identification of rice diseases using deep convolutional neural networks. Neurocomputing, Volume 267, 6 December 2017, Pages 378-384.
  • [18] Harshadkumar B. Prajapati, Jitesh P. Shah and Vipul K. Dabhi. (2017). Detection and classification of rice plant diseases. Intelligent Decision Technologies 11 357–373.
  • [19] Chowdhury R. Rahman, Preetom S. Arko, Mohammed E. Ali, Mohammad A. Iqbal Khan, Sajid H. Apon, Farzana Nowrin, Abu Wasif, Identification and recognition of rice diseases and pests using convolutional neural networks, Biosystems Engineering, Volume 194, 2020, Pages 112-120, ISSN 1537-5110, https://doi.org/10.1016/j.biosystemseng.2020.03.020.
  • [20] DeChant, C., Wiesner-Hanks, T., Chen, S., Stewart, E. L., Yosinski, J., Gore, M. A., et al. (2017). Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning. Phytopathology, 107(11), 1426e1432.
  • [21] Liu, B., Zhang, Y., He, D., & Li, Y. (2018). Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry, 10(1), 11.
  • [22] Lu, Y., Yi, S., Zeng, N., Liu, Y., & Zhang, Y. (2017). Identification of rice diseases using deep convolutional neural networks. Neurocomputing, 267, 378e384.
  • [23] Atole, R. R., & Park, D. (2018). A multiclass deep convolutional neural network classifier for detection of common rice plant anomalies. International Journal of Advanced Computer Science and Applications, 9(1), 6770.
  • [24] Y. LeCun, Y. Bengio and G. Hinton, “Deep Learning”, Nature, vol. 521, pp. 436-444, 2015
  • [25] H. Durmus,, E. O. Gunes, and M. Kırcı, “Disease detection on the leaves of the tomato plants by using deep learning”, In Agro-Geoinformatics, IEEE 6th International Conference on, pp. 1-5, 2017.
There are 25 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Araştırma Makalesi
Authors

İrfan Ökten 0000-0001-9898-7859

Uğur Yüzgeç 0000-0002-5364-6265

Publication Date March 24, 2022
Submission Date October 25, 2021
Acceptance Date February 11, 2022
Published in Issue Year 2022 Volume: 11 Issue: 1

Cite

IEEE İ. Ökten and U. Yüzgeç, “Evrişimli Sinir Ağı ile Çeltik Bitkisi Hastalığının Tespiti”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 11, no. 1, pp. 203–217, 2022, doi: 10.17798/bitlisfen.1014393.

Bitlis Eren University
Journal of Science Editor
Bitlis Eren University Graduate Institute
Bes Minare Mah. Ahmet Eren Bulvari, Merkez Kampus, 13000 BITLIS