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Comparison of Standard and Pretrained CNN Models for Potato, Cotton, Bean and Banana Disease Detection

Year 2021, , 86 - 99, 18.12.2021
https://doi.org/10.46572/naturengs.1007532

Abstract

Plant diseases lead to a significant decrease in product efficiency and economic losses for producers. However, early detection of plant diseases plays an important role in preventing these losses. Today, Convolutional Neural Network (CNN) models are widely used for image processing in many fields such as face recognition, climate, health, and agriculture. But in these models, the weights of the layers are randomly initialized during training, which increases training time and decreases performance. With the method known as Transfer Learning in the literature, CNN models are trained on large databases such as ImageNet. Then, pretrained CNN models are created using the weights obtained in this training. Thus, training time decreases while performance improves. In this study, standard and pretrained versions of popular CNN models DarkNet-19, GoogleNet, Inception-v3, Resnet-18, and ShuffleNet have been used for automatic classification of diseases from leaf images of potato, cotton, bean, and banana. In the experimental study, the classification performances of all these standard and pretrained CNN models are presented comparatively. Experimental results have shown that the performance of CNN models is significantly improved by transfer learning, even in a small number of epochs.

References

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  • [6] Sert, E (2021). A deep learning based approach for the detection of diseases in pepper and potato leaves. Anadolu Tarım Bilimleri Dergisi, 36(2), 167-178.
  • [7] Khan, M. A., Akram, T., Sharif, M., Awais, M., Javed, K., Ali, H., and Saba, T. (2018). CCDF: Automatic system for segmentation and recognition of fruit crops diseases based on correlation coefficient and deep CNN features. Computers and electronics in agriculture, 155, 220-236.
  • [8] Aksoy, B., Halis, H. D., & Salman, O. K. M. (2020). Elma Bitkisindeki Hastalıkların Yapay Zekâ Yöntemleri ile Tespiti ve Yapay Zekâ Yöntemlerinin Performanslarının Karşılaştırılması. International Journal of Engineering and Innovative Research, 2(3), 194-210.
  • [9] Hassan, S. M., Maji, A. K., Jasiński, M., Leonowicz, Z., and Jasińska, E. (2021). Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach. Electronics, 10(12), 1388.
  • [10] Arivazhagan, S., and Ligi, S. V. (2018). Mango leaf diseases identification using convolutional neural network. International Journal of Pure and Applied Mathematics, 120(6), 11067-11079.
  • [11] Priyadharshini, R. A., Arivazhagan, S., Arun, M., and Mirnalini, A. (2019). Maize leaf disease classification using deep convolutional neural networks. Neural Computing and Applications, 31(12), 8887-8895.
  • [12] Dinata, M. I., Nugroho, S. M. S., and Rachmadi, R. F. (2021, June). Classification of Strawberry Plant Diseases with Leaf Image Using CNN, International Conference on Artificial Intelligence and Computer Science Technology (ICAICST) IEEE, Yogyakarta, Indonesia, 68-72.
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  • [17] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, USA, 2818-2826.
  • [18] Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L. C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition, Salt Lake City, USA, 4510-4520.
  • [19] He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, USA, 770-778.
  • [20] Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018). Shufflenet: An extremely efficient convolutional neural network for mobile devices. In Proceedings of the IEEE conference on computer vision and pattern recognition, Salt Lake City, USA, 6848-6856.
  • [21] Fırıldak, K., and Talu, M. F. (2019). Evrişimsel Sinir Ağlarında Kullanılan Transfer Öğrenme Yaklaşımlarının İncelenmesi. Computer Science, 4(2), 88-95.
  • [22] Nath, S. (2021). Potato Leaf Disease Detection. Kaggle Data, version 1. Retrieved September 20, 2021 from https://www.kaggle.com/sayannath235/potato-leaf-disease-detection/metadata.
  • [23] Karim, S. (2021). Cotton Leaf Disease Dataset. Kaggle Data, version 1. Retrieved September 20, 2021 from https://www.kaggle.com/seroshkarim/cotton-leaf-disease-dataset/metadata.
  • [24] Rastogi, P. (2021). Bean leaf dataset. Kaggle Data, version 1. Retrieved September 20, 2021 from https://www.kaggle.com/prakharrastogi534/bean-leaf-dataset/metadata.
  • [25] Mahmud, K. A. (2021). Banana Leaf Dataset. Kaggle Data, version 2. Retrieved September 20, 2021 from https://www.kaggle.com/kaiesalmahmud/banana-leaf-dataset.
Year 2021, , 86 - 99, 18.12.2021
https://doi.org/10.46572/naturengs.1007532

Abstract

References

  • [1] Türkoğlu, M., Hanbay, K., Sivrikaya, I. S., And Hanbay, D. (2021). Derin Evrişimsel Sinir Ağı Kullanılarak Kayısı Hastalıklarının Sınıflandırılması. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 9(1), 334-345.
  • [2] Aslan, M. (2021). Derin Öğrenme ile Bitki Hastalıklarının Tespiti. Avrupa Bilim ve Teknoloji Dergisi, (23), 540-546.
  • [3] Chen, J., Zhang, D., Zeb, A., and Nanehkaran, Y. A. (2021). Identification of rice plant diseases using lightweight attention networks. Expert Systems with Applications, 169, 114514.
  • [4] Shrivastava, V. K., Pradhan, M. K., Minz, S., & Thakur, M. P. (2019). Rice plant disease classification using transfer learning of deep convolution neural network. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, XLII-3/W6, 631–635.
  • [5] Abbas, A., Jain, S., Gour, M., and Vankudothu, S. (2021). Tomato plant disease detection using transfer learning with C-GAN synthetic images. Computers and Electronics in Agriculture, 187, 106279.
  • [6] Sert, E (2021). A deep learning based approach for the detection of diseases in pepper and potato leaves. Anadolu Tarım Bilimleri Dergisi, 36(2), 167-178.
  • [7] Khan, M. A., Akram, T., Sharif, M., Awais, M., Javed, K., Ali, H., and Saba, T. (2018). CCDF: Automatic system for segmentation and recognition of fruit crops diseases based on correlation coefficient and deep CNN features. Computers and electronics in agriculture, 155, 220-236.
  • [8] Aksoy, B., Halis, H. D., & Salman, O. K. M. (2020). Elma Bitkisindeki Hastalıkların Yapay Zekâ Yöntemleri ile Tespiti ve Yapay Zekâ Yöntemlerinin Performanslarının Karşılaştırılması. International Journal of Engineering and Innovative Research, 2(3), 194-210.
  • [9] Hassan, S. M., Maji, A. K., Jasiński, M., Leonowicz, Z., and Jasińska, E. (2021). Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach. Electronics, 10(12), 1388.
  • [10] Arivazhagan, S., and Ligi, S. V. (2018). Mango leaf diseases identification using convolutional neural network. International Journal of Pure and Applied Mathematics, 120(6), 11067-11079.
  • [11] Priyadharshini, R. A., Arivazhagan, S., Arun, M., and Mirnalini, A. (2019). Maize leaf disease classification using deep convolutional neural networks. Neural Computing and Applications, 31(12), 8887-8895.
  • [12] Dinata, M. I., Nugroho, S. M. S., and Rachmadi, R. F. (2021, June). Classification of Strawberry Plant Diseases with Leaf Image Using CNN, International Conference on Artificial Intelligence and Computer Science Technology (ICAICST) IEEE, Yogyakarta, Indonesia, 68-72.
  • [13] Qin, Z., Yu, F., Liu, C., and Chen, X. (2018). How convolutional neural network see the world-A survey of convolutional neural network visualization methods. arXiv preprint arXiv:1804.11191.
  • [14] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90.
  • [15] Redmon, J., and Farhadi, A. (2017). YOLO9000: better, faster, stronger. In Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, HI, USA, 7263-7271.
  • [16] Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... and Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, Boston, USA, 1-9.
  • [17] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, USA, 2818-2826.
  • [18] Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L. C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition, Salt Lake City, USA, 4510-4520.
  • [19] He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, USA, 770-778.
  • [20] Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018). Shufflenet: An extremely efficient convolutional neural network for mobile devices. In Proceedings of the IEEE conference on computer vision and pattern recognition, Salt Lake City, USA, 6848-6856.
  • [21] Fırıldak, K., and Talu, M. F. (2019). Evrişimsel Sinir Ağlarında Kullanılan Transfer Öğrenme Yaklaşımlarının İncelenmesi. Computer Science, 4(2), 88-95.
  • [22] Nath, S. (2021). Potato Leaf Disease Detection. Kaggle Data, version 1. Retrieved September 20, 2021 from https://www.kaggle.com/sayannath235/potato-leaf-disease-detection/metadata.
  • [23] Karim, S. (2021). Cotton Leaf Disease Dataset. Kaggle Data, version 1. Retrieved September 20, 2021 from https://www.kaggle.com/seroshkarim/cotton-leaf-disease-dataset/metadata.
  • [24] Rastogi, P. (2021). Bean leaf dataset. Kaggle Data, version 1. Retrieved September 20, 2021 from https://www.kaggle.com/prakharrastogi534/bean-leaf-dataset/metadata.
  • [25] Mahmud, K. A. (2021). Banana Leaf Dataset. Kaggle Data, version 2. Retrieved September 20, 2021 from https://www.kaggle.com/kaiesalmahmud/banana-leaf-dataset.
There are 25 citations in total.

Details

Primary Language English
Journal Section Research Articles
Authors

Soner Kızıloluk 0000-0002-0381-9631

Publication Date December 18, 2021
Submission Date October 9, 2021
Acceptance Date November 30, 2021
Published in Issue Year 2021

Cite

APA Kızıloluk, S. (2021). Comparison of Standard and Pretrained CNN Models for Potato, Cotton, Bean and Banana Disease Detection. NATURENGS, 2(2), 86-99. https://doi.org/10.46572/naturengs.1007532