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DERİN ÖĞRENME YÖNTEMİ İLE BİTKİ YAPRAĞI HASTALIK SINIFLANDIRMA ÇALIŞMASI PERFORMANS ANALİZİ

Year 2022, Volume: 25 Issue: 2, 126 - 137, 03.06.2022

İpek Atik

https://doi.org/10.17780/ksujes.1096541
Cited By: 1

Abstract

Bitkilerin yetiştirilme süreci zahmetli ve uzun süren bir işlemdir. Bitki yetiştiriciliği ile uğraşan kişilerin en önemli sorunlarından biri bitki hastalığıdır. Hastalıkla mücadelede ilk olarak yapılması gereken hastalığın tanınmasıdır. Hastalığın hızlı bir şekilde tespit edip gereken önlemleri hızlı bir şekilde alabilmek oldukça önemlidir. Çalışmada domates yapraklarındaki hastalık belirlenmesinde derin öğrenme yöntemleri kullanılmıştır. Çalışmada veri seti hastalık olarak 10 sınıftan oluşan toplam 18.160 domates yaprağı görüntüsü bulunmadır. Görüntü hastalık sınıflandırmasında derin evrişimli sinir ağları (ESA) modellerden ön eğitimli ağlar olan GoogleNet, AlexNet, SqueezeNet, ShuffleNet ve ResNet-18 modelleri kullanılmıştır. Modellerde eğitim veri seti %70 eğitim, %15 doğrulama ve %15 test olarak ayrılmıştır. Eğitilen ağların test verisi ile performans ölçütleri doğruluk, kesinlik, özgüllük ve f-skor değerleri hesaplanmıştır. Modellerin doğruluk oranları AlexNet, GoogleNet, ShuffleNet, SqueezeNet ve ResNet-18 için sırasıyla %93.93, %95.18, %94.82, %94.29 ve %81.79 olarak elde edilmiştir. Yapılan analizlere göre ön eğitimli ağların domates yaprağı hastalık sınıflandırma çalışmasında en iyi performans gösteren modelin GoogleNet olduğu görülmüştür.

Keywords

Derin Öğrenme evrişimsel sinir ağları görüntü sınıflandırma domates bitkisi hastalık

References

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  • Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., & Stefanović, D. (2016). Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification. Computational Intelligence and Neuroscience, 2016.
  • Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017). Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11231
  • Too, E. C., Yujian, L., Njuki, S., & Yingchun, L. (2019). A comparative study of fine-tuning deep learning models for plant disease identification. Comput. Electron. Agric., 161, 272–279.
  • Ucar, F., & Korkmaz, D. (2020). COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. Medical Hypotheses, 140, 109761–109761. doi:10.1016/j.mehy.2020.109761
  • Wang, G., Sun, Y., & Wang, J. (2017). Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning. Computational Intelligence and Neuroscience, 2017, 1–8. doi:10.1155/2017/2917536
  • Yamamoto, K., Togami, T., & Yamaguchi, N. (2017). Super-Resolution of Plant Disease Images for the Acceleration of Image-based Phenotyping and Vigor Diagnosis in Agriculture. Sensors, 17(11). doi:10.3390/s17112557
  • Zhang, S., Huang, W., & Zhang, C. (2019). Three-channel convolutional neural networks for vegetable leaf disease recognition. Cognitive Systems Research, 53, 31–41.
  • Zhang, X., Zhou, X., Lin, M., & Sun, J. (2018). Shufflenet: An extremely efficient convolutional neural network for mobile devices (pp. 6848–6856). Presented at the Proceedings of the IEEE conference on computer vision and pattern recognition.

Year 2022, Volume: 25 Issue: 2, 126 - 137, 03.06.2022

İpek Atik

https://doi.org/10.17780/ksujes.1096541
Cited By: 1

Abstract

References

  • Acikgoz, H. (2022). A novel approach based on integration of convolutional neural networks and deep feature selection for short-term solar radiation forecasting. Applied Energy, (305). doi:https://doi.org/10.1016/j.apenergy.2021.117912.
  • Arivazhagan, S., Shebiah, R. N., Ananthi, S. N., & Varthini, S. V. (2013). Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agricultural Engineering International: The CIGR Journal, 15, 211–217.
  • Arsenovic, M., Karanovic, M., Sladojevic, S., Anderla, A., & Stefanovic, D. (2019). Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection. Symmetry, 11(7). doi:10.3390/sym11070939
  • Athanikar, G., & Badar, M. P. (2016). Potato Leaf Diseases Detection and Classification System Mr. Atik, I. (2022a). Classification of Electronic Components Based on Convolutional Neural Network Architecture. Energies, 15(7). doi:10.3390/en15072347
  • Atik, I. (2022b). Performance Comparison of Pre-Trained Convolutional Neural Networks in Flower Image Classification. Avrupa Bilim ve Teknoloji Dergisi, (35), 315–321. doi:10.31590/ejosat.1082023
  • Brahimi, M., Boukhalfa, K., & Moussaoui, A. (2017a). Deep Learning for Tomato Diseases: Classification and Symptoms Visualization. Applied Artificial Intelligence, 31(4), 299–315. doi:10.1080/08839514.2017.1315516
  • Ciresan, D. C., Meier, U., Masci, J., Gambardella, L. M., & Schmidhuber, J. (2011). Flexible, High Performance Convolutional Neural Networks for Image Classification. IJCAI.
  • Durmuş, H., Güneş, E. O., & Kırcı, M. (2017). Disease detection on the leaves of the tomato plants by using deep learning. In 2017 6th International Conference on Agro-Geoinformatics (pp. 1–5). doi:10.1109/Agro-Geoinformatics.2017.8047016
  • Dyrmann, M., Karstoft, H., & Midtiby, H. S. (2016). Plant species classification using deep convolutional neural network. Biosystems Engineering, 151, 72–80. doi:10.1016/j.biosystemseng.2016.08.024
  • Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311–318. doi:https://doi.org/10.1016/j.compag.2018.01.009
  • 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). doi:10.3390/s17092022
  • G, G., & J, A. P. (2019). Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Computers & Electrical Engineering, 76, 323–338. doi:https://doi.org/10.1016/j.compeleceng.2019.04.011
  • H. Sabrol, & K. Satish. (2016). Tomato plant disease classification in digital images using classification tree (pp. 1242–1246). Presented at the 2016 International Conference on Communication and Signal Processing (ICCSP). doi:10.1109/ICCSP.2016.7754351
  • Hoo, Z. H., Candlish, J., & Teare, D. (2017). What is an ROC curve? Emergency Medicine Journal, 34(6), 357–359. doi:10.1136/emermed-2017-206735
  • Kaggle. (2021, December 6). Kaggle. Kaggle data set. dataset. Retrieved from https://www.kaggle.com/datasets(Erişim tarihi:15.01.2022) Krizhevsky, A. (2012). Convolutional Deep Belief Networks on CIFAR-10.https://www.cs.toronto.edu/~kriz/conv-cifar10-aug2010.pdf
  • Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097–1105.
  • Liu, Y., Tang, F., Zhou, D., Meng, Y., & Dong, W. (2016). Flower classification via convolutional neural network. In 2016 IEEE International Conference on Functional-Structural Plant Growth Modeling, Simulation, Visualization and Applications (FSPMA) (pp. 110–116). doi:10.1109/FSPMA.2016.7818296
  • Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using Deep Learning for Image-Based Plant Disease Detection. Frontiers in Plant Science, 7.
  • Öter, A., Aydoğan, O., KIYMIK, M., & Tuncel, D. (2016). Tıkayıcı Uyku Apnesinin Yapay Sinir Ağları ve Morfolojik Filtreler kullanılarak Sınıflandırılması İçin Yeni Yöntem. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 19, 52. doi:10.17780/ksujes.74055
  • Öztürk, M., & Paksoy, T. (2018). Buğday Tipi Sınıflandırma için Yapay Sinir Ağı Uygulaması: Yeni Bir Yapay Zeka Eğitimi Yazılımı. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 21, 246–257. doi:10.17780/ksujes.332770 Sannakki, S., Rajpurohit, V. S., Sumira, F., & Venkatesh, H. (2013). A neural network approach for disease forecasting in grapes using weather parameters. In 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT) (pp. 1–5). doi:10.1109/ICCCNT.2013.6726613
  • Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., & Stefanović, D. (2016). Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification. Computational Intelligence and Neuroscience, 2016.
  • Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017). Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11231
  • Too, E. C., Yujian, L., Njuki, S., & Yingchun, L. (2019). A comparative study of fine-tuning deep learning models for plant disease identification. Comput. Electron. Agric., 161, 272–279.
  • Ucar, F., & Korkmaz, D. (2020). COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. Medical Hypotheses, 140, 109761–109761. doi:10.1016/j.mehy.2020.109761
  • Wang, G., Sun, Y., & Wang, J. (2017). Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning. Computational Intelligence and Neuroscience, 2017, 1–8. doi:10.1155/2017/2917536
  • Yamamoto, K., Togami, T., & Yamaguchi, N. (2017). Super-Resolution of Plant Disease Images for the Acceleration of Image-based Phenotyping and Vigor Diagnosis in Agriculture. Sensors, 17(11). doi:10.3390/s17112557
  • Zhang, S., Huang, W., & Zhang, C. (2019). Three-channel convolutional neural networks for vegetable leaf disease recognition. Cognitive Systems Research, 53, 31–41.
  • Zhang, X., Zhou, X., Lin, M., & Sun, J. (2018). Shufflenet: An extremely efficient convolutional neural network for mobile devices (pp. 6848–6856). Presented at the Proceedings of the IEEE conference on computer vision and pattern recognition.
There are 28 citations in total.

Details

Primary Language Turkish
Subjects Electrical Engineering
Journal Section Computer Engineering
Authors

İpek Atik 0000-0002-9761-1347

Publication Date June 3, 2022
Submission Date March 31, 2022
Published in Issue Year 2022Volume: 25 Issue: 2

Cite

APA Atik, İ. (2022). DERİN ÖĞRENME YÖNTEMİ İLE BİTKİ YAPRAĞI HASTALIK SINIFLANDIRMA ÇALIŞMASI PERFORMANS ANALİZİ. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 25(2), 126-137. https://doi.org/10.17780/ksujes.1096541

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