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Vol. 7, No. 2, May 2022

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Convolutional Neural Network (CNN) Models for Crop Diseases Classification

https://doi.org/10.22219/kinetik.v7i2.1443
Deni Sutaji
Universitas Muhammadiyah Gresik; Gazi University, Ankara, Turkey
Harunur Rosyid
Universitas Muhammadiyah Gresik

Corresponding Author(s) : Deni Sutaji

sutaji.deni@umg.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 7, No. 2, May 2022

Abstract

Crop diseases have a significant impact on agricultural production. As a result, early diagnosis of crop diseases is critical. Deep learning approaches are now promising to improve disease detection. Convolutional Neural Network (CNN) models can detect crop disease using images with automatic feature extraction. This study proposes crop disease classification considering ten pre-trained CNN models. Fine-tuning for each model was conducted in the Plant Village dataset. The experimental results show that fine-tuning improves the model’s performance with an average accuracy of 8.85%. The best CNN model was DenseNet121, with 94.48% and 98.97% accuracy for freezing all layers and unfreezing last block convolution layers. Moreover, fine-tuning produces less time-consuming with an average of 2.20 hours. VGG19 is the less time-consuming reduction by 8 hours. On the other hand, MobileNetV2 is the second-best performance model with less time-consuming than DenseNet121, and produces fewer parameters, which is affordable for embedding it to mobile devices.

Keywords

CNN Models Crop Disease Classification Unfreeze Layer
Sutaji, D., & Rosyid, H. (2022). Convolutional Neural Network (CNN) Models for Crop Diseases Classification. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 7(2). https://doi.org/10.22219/kinetik.v7i2.1443
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References
  1. M. Turkoglu, B. Yanikoğlu, and D. Hanbay, “PlantDiseaseNet: convolutional neural network ensemble for plant disease and pest detection,” Signal, Image and Video Processing, 2021, doi: 10.1007/s11760-021-01909-2.
  2. N. Ahmad, S. Asghar, and S. A. Gillani, “Transfer learning-assisted multi-resolution breast cancer histopathological images classification,” Visual Computer, 2021, doi: 10.1007/s00371-021-02153-y.
  3. S. Kaur, S. Pandey, and S. Goel, “Plants Disease Identification and Classification Through Leaf Images: A Survey,” Archives of Computational Methods in Engineering, vol. 26, no. 2, pp. 507–530, Apr. 2019, doi: 10.1007/s11831-018-9255-6.
  4. S. Sachar and A. Kumar, “Survey of feature extraction and classification techniques to identify plant through leaves,” Expert Systems with Applications, vol. 167. Elsevier Ltd, Apr. 01, 2021. doi: 10.1016/j.eswa.2020.114181.
  5. V. Singh, N. Sharma, and S. Singh, “A review of imaging techniques for plant disease detection,” Artificial Intelligence in Agriculture, vol. 4, pp. 229–242, 2020, doi: 10.1016/j.aiia.2020.10.002.
  6. J. Lu, L. Tan, and H. Jiang, “Review on convolutional neural network (CNN) applied to plant leaf disease classification,” Agriculture (Switzerland), vol. 11, no. 8. MDPI AG, Aug. 01, 2021. doi: 10.3390/agriculture11080707.
  7. A. Abade, P. A. Ferreira, and F. de Barros Vidal, “Plant diseases recognition on images using convolutional neural networks: A systematic review,” Computers and Electronics in Agriculture, vol. 185. Elsevier B.V., Jun. 01, 2021. doi: 10.1016/j.compag.2021.106125.
  8. R. Manavalan, “Automatic identification of diseases in grains crops through computational approaches: A review,” Computers and Electronics in Agriculture, vol. 178. Elsevier B.V., Nov. 01, 2020. doi: 10.1016/j.compag.2020.105802.
  9. Z. Iqbal, M. A. Khan, M. Sharif, J. H. Shah, M. H. ur Rehman, and K. Javed, “An automated detection and classification of citrus plant diseases using image processing techniques: A review,” Computers and Electronics in Agriculture, vol. 153. Elsevier B.V., pp. 12–32, Oct. 01, 2018. doi: 10.1016/j.compag.2018.07.032.
  10. L. C. Ngugi, M. Abelwahab, and M. Abo-Zahhad, “Recent advances in image processing techniques for automated leaf pest and disease recognition – A review,” Information Processing in Agriculture, vol. 8, no. 1. China Agricultural University, pp. 27–51, Mar. 01, 2021. doi: 10.1016/j.inpa.2020.04.004.
  11. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Advances in Neural Information Processing Systems 25, 2012, pp. 1–9. [Online]. Available: http://code.google.com/p/cuda-convnet/
  12. C. Szegedy et al., “Going Deeper with Convolutions,” Sep. 2014, [Online]. Available: http://arxiv.org/abs/1409.4842
  13. K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” Sep. 2014, [Online]. Available: http://arxiv.org/abs/1409.1556
  14. K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” Dec. 2015, [Online]. Available: http://arxiv.org/abs/1512.03385
  15. C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision,” Dec. 2015, [Online]. Available: http://arxiv.org/abs/1512.00567
  16. G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely Connected Convolutional Networks,” Aug. 2016, [Online]. Available: http://arxiv.org/abs/1608.06993
  17. F. Chollet, “Xception: Deep Learning with Depthwise Separable Convolutions,” 2017.
  18. M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” Jan. 2018, [Online]. Available: http://arxiv.org/abs/1801.04381
  19. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi, “Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning,” Feb. 2016, [Online]. Available: http://arxiv.org/abs/1602.07261
  20. B. Zoph, V. Vasudevan, J. Shlens, and Q. v. Le, “Learning Transferable Architectures for Scalable Image Recognition,” Jul. 2017, [Online]. Available: http://arxiv.org/abs/1707.07012
  21. M. Tan and Q. v Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” 2019.
  22. M. Khanramaki, E. Askari Asli-Ardeh, and E. Kozegar, “Citrus pests classification using an ensemble of deep learning models,” Computers and Electronics in Agriculture, vol. 186, Jul. 2021, doi: 10.1016/j.compag.2021.106192.
  23. S. P. Mohanty, D. P. Hughes, and M. Salathé, “Using deep learning for image-based plant disease detection,” Frontiers in Plant Science, vol. 7, no. September, Sep. 2016, doi: 10.3389/fpls.2016.01419.
  24. S. Sladojevic, M. Arsenovic, A. Anderla, D. Culibrk, and D. Stefanovic, “Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification,” Computational Intelligence and Neuroscience, vol. 2016, 2016, doi: 10.1155/2016/3289801.
  25. J. Ma, K. Du, F. Zheng, L. Zhang, Z. Gong, and Z. Sun, “A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network,” Computers and Electronics in Agriculture, vol. 154, pp. 18–24, Nov. 2018, doi: 10.1016/j.compag.2018.08.048.
  26. S. Zhang, S. Zhang, C. Zhang, X. Wang, and Y. Shi, “Cucumber leaf disease identification with global pooling dilated convolutional neural network,” Computers and Electronics in Agriculture, vol. 162, pp. 422–430, Jul. 2019, doi: 10.1016/j.compag.2019.03.012.
  27. C. Wang, P. Du, H. Wu, J. Li, C. Zhao, and H. Zhu, “A cucumber leaf disease severity classification method based on the fusion of DeepLabV3+ and U-Net,” Computers and Electronics in Agriculture, vol. 189, Oct. 2021, doi: 10.1016/j.compag.2021.106373.
  28. H. T. Rauf, B. A. Saleem, M. I. U. Lali, M. A. Khan, M. Sharif, and S. A. C. Bukhari, “A citrus fruits and leaves dataset for detection and classification of citrus diseases through machine learning,” Data in Brief, vol. 26, Oct. 2019, doi: 10.1016/j.dib.2019.104340.
  29. U. Barman, R. D. Choudhury, D. Sahu, and G. G. Barman, “Comparison of convolution neural networks for smartphone image based real time classification of citrus leaf disease,” Computers and Electronics in Agriculture, vol. 177, Oct. 2020, doi: 10.1016/j.compag.2020.105661.
  30. R. Gandhi, S. Nimbalkar, N. Yelamanchili, and S. Ponkshe, “Plant Disease Detection Using CNNs and GANs as an Augmenting Approach,” May 2018.
  31. E. C. Too, L. Yujian, S. Njuki, and L. Yingchun, “A comparative study of fine-tuning deep learning models for plant disease identification,” Computers and Electronics in Agriculture, vol. 161, pp. 272–279, Jun. 2019, doi: 10.1016/j.compag.2018.03.032.
  32. G. Geetharamani, P. J. Arun, M. Agarwalc, and S. K. Gupta, “Identification of plant leaf diseases using a nine-layer deep convolutional neural network,” Computers and Electrical Engineering, vol. 76, pp. 323–338, Jun. 2019, doi: 10.1016/j.compeleceng.2019.04.011.
  33. Ü. Atila, M. Uçar, K. Akyol, and E. Uçar, “Plant leaf disease classification using EfficientNet deep learning model,” Ecological Informatics, vol. 61, Mar. 2021, doi: 10.1016/j.ecoinf.2020.101182.
  34. S. Vallabhajosyula, V. Sistla, and V. K. K. Kolli, “Transfer learning-based deep ensemble neural network for plant leaf disease detection,” Journal of Plant Diseases and Protection, 2021, doi: 10.1007/s41348-021-00465-8.
  35. I. Ahmad, M. Hamid, S. Yousaf, S. T. Shah, and M. O. Ahmad, “Optimizing pretrained convolutional neural networks for tomato leaf disease detection,” Complexity, vol. 2020, 2020, doi: 10.1155/2020/8812019.
  36. J. Chen, D. Zhang, and Y. A. Nanehkaran, “Identifying plant diseases using deep transfer learning and enhanced lightweight network,” Multimedia Tools and Applications, vol. 79, no. 41–42, pp. 31497–31515, Nov. 2020, doi: 10.1007/s11042-020-09669-w.
  37. J. Chen, J. Chen, D. Zhang, Y. Sun, and Y. A. Nanehkaran, “Using deep transfer learning for image-based plant disease identification,” Computers and Electronics in Agriculture, vol. 173, Jun. 2020, doi: 10.1016/j.compag.2020.105393.
  38. V. Tiwari, R. C. Joshi, and M. K. Dutta, “Dense convolutional neural networks based multiclass plant disease detection and classification using leaf images,” Ecological Informatics, vol. 63, Jul. 2021, doi: 10.1016/j.ecoinf.2021.101289.
  39. R. Gajjar, N. Gajjar, V. J. Thakor, N. P. Patel, and S. Ruparelia, “Real-time detection and identification of plant leaf diseases using convolutional neural networks on an embedded platform,” Visual Computer, 2021, doi: 10.1007/s00371-021-02164-9.
  40. J. Chen, D. Zhang, M. Suzauddola, and A. Zeb, “Identifying crop diseases using attention embedded MobileNet-V2 model,” Applied Soft Computing, vol. 113, Dec. 2021, doi: 10.1016/j.asoc.2021.107901.
  41. E. Prasetyo, N. Suciati, and C. Fatichah, “Multi-level residual network VGGNet for fish species classification,” Journal of King Saud University - Computer and Information Sciences, 2021, doi: 10.1016/j.jksuci.2021.05.015.
  42. V. Kumar, H. Arora, Harsh, and J. Sisodia, “ResNet-based approach for detection classification of plant leaf diseases,” in Proceedings of the International Conference on Electronics and Sustainable Communication Systems (ICESC 2020), 2020, pp. 495–502. Accessed: Apr. 26, 2022. [Online]. Available: https://doi.org/10.1109/ICESC48915.2020.9155585
  43. R. Sujatha, J. M. Chatterjee, N. Z. Jhanjhi, and S. N. Brohi, “Performance of deep learning vs machine learning in plant leaf disease detection,” Microprocessors and Microsystems, vol. 80, Feb. 2021, doi: 10.1016/j.micpro.2020.103615.
  44. M. A. Moid and M. A. Chaurasia, “Transfer Learning-based Plant Disease Detection and Diagnosis System using Xception,” in Proceedings of the 5th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2021, 2021, pp. 451–455. doi: 10.1109/I-SMAC52330.2021.9640694.
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