skip to main content

Automatic detection of crop diseases using gamma transformation for feature learning with a deep convolutional autoencoder

1Research Center for Data and Information Sciences, National Research and Innovation Agency (BRIN), Indonesia

2Research Center for Artificial Intelligence and Cyber Security, National Research and Innovation Agency (BRIN), Indonesia

Received: 23 Jun 2021; Published: 24 Sep 2024.
Open Access Copyright (c) 2024 Vicky Zilvan, Ade Ramdan, Ahmad Afif Supianto, Ana Heryana, Andria Arisal, Asri Rizki Yuliani, Dikdik Krisnandi, Endang Suryawati, Raden Budiarianto Suryo Kusumo, Raden Sandra Yuawana, Hilman F. Pardede
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract
Precision agriculture is a management strategy for sustaining and increasing the production of agricultural commodities. One of its implementations is for crop disease detection. Currently, deep learning methods have become widespread methods for the automatic detection of crop diseases. Most deep learning methods showed better performance when using an original image in raw form as inputs. However, the original image of crop diseases may appear similar between one disease to another.  Therefore, the deep learning methods may misclassify the data. To deal with these, we propose the gamma transformation with a deep convolutional autoencoder to extract good features from the original image data. We use the output of the gamma transformation with a deep convolutional autoencoder as inputs to a classifier for the automatic detection of crop diseases. Our experiments show that the average accuracies of our method improve the performance of crop disease detection compared to only using raw data as inputs.
Fulltext Email colleagues
Keywords: deep learning; gamma transformation; unsupervised feature learning; deep convolutional autoencoder; automatic detection of crop diseases
Funding: Insinas Grant 2021 from the Indonesian Ministry of Research, Technology, and Higher Education

Article Metrics:

  1. Andrew McGuire. (2015) The Essentials of Sustaining Agricultural Production. [Online]. Available: https://csanr.wsu.edu/essentials-of-sustaining-ag-production/
  2. J. G. A. Barbedo, “A review on the main challenges in automatic plant disease identification based on visible range images,” Biosystems Engineering, vol. 144, pp. 52 – 60, 2016. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1537511015302476
  3. H. F. Pardede, E. Suryawati, R. Sustika, and V. Zilvan, “Unsupervised convolutional autoencoder-based feature learning for automatic detection of plant diseases,” in 2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA), Nov 2018, pp. 158–162
  4. A. Ramcharan, K. Baranowski, P. McCloskey, B. Ahmed, J. Legg, and D. P. Hughes, “Deep learning for image-based cassava disease detection,” Frontiers in Plant Science, vol. 8, p. 1852, 2017. [Online]. Available: https://www.frontiersin.org/article/10.3389/fpls.2017.01852
  5. B. S. Kusumo, A. Heryana, O. Mahendra, and H. F. Pardede, “Machine learning-based for automatic detection of corn-plant diseases using image processing,” in 2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA), 2018, pp. 93–97
  6. Y. Dandawate and R. Kokare, “An automated approach for classification of plant diseases towards development of futuristic decision support system in indian perspective,” in 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2015, pp. 794–799
  7. D. G. Tsolakidis, D. I. Kosmopoulos, and G. Papadourakis, “Plant leaf recognition using zernike moments and histogram of oriented gradients,” in Artificial Intelligence: Methods and Applications, A. Likas, K. Blekas, and D. Kalles, Eds. Cham: Springer International Publishing, 2014, pp. 406–417
  8. E. Suryawati, R. Sustika, R. S. Yuwana, A. Subekti, and H. F. Pardede, “Deep structured convolutional neural network for tomato diseases detection,” in 2018 International Conference on Advanced ComputerScience and Information Systems (ICACSIS), 2018, pp. 385–390
  9. K. P. Ferentinos, "Deep learning models for plant disease detection and diagnosis," Computers and Electronics in Agriculture, vol. 145, pp. 311 - 318, 2018. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0168169917311742
  10. D. Krisnandi, H. Pardede, R. Yuwana, V. Zilvan, A. Heryana, F. Fauziah, and V. Rahadi, "Diseases classification for tea plant using concatenated convolution neural network," CommIT (Communication and Information Technology) Journal, vol. 13, 10 2019
  11. A. Fuentes, S. Yoon, S. Kim, and D. Park, "A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition," Sensors, vol. 17, no. 9, p. 2022, Sep 2017. [Online]. Available: http://dx.doi.org/10.3390/s17092022
  12. J. Carranza-Rojas, H. Goeau, P. Bonnet, E. Mata-Montero, and A. Joly, "Going deeper in the automated identification of herbarium specimens," BMC Evolutionary Biology, vol. 17, no. 1, p. 181, Aug 2017. [Online]. Available: https://doi.org/10.1186/s12862-017-1014-z
  13. R. S. Yuwana, E. Suryawati, V. Zilvan, A. Ramdan, H. F. Pardede, and F. Fauziah, "Multi-condition training on deep convolutional neural networks for robust plant diseases detection," in 2019 International Conference on Computer, Control, Informatics and its Applications (IC3INA), 2019, pp. 30-35.14
  14. G. L. Grinblat, L. C. Uzal, M. G. Larese, and P. M. Granitto, "Deep learning for plant identification using vein morphological patterns," Computers and Electronics in Agriculture, vol. 127, pp. 418 - 424, 2016. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0168169916304665
  15. Z. Mao, J. Chen, and M. Yang, "Multi-branch structure for hierarchical classification in plant disease recognition," in Pattern Recognition and Computer Vision, Z. Lin, L. Wang, J. Yang, G. Shi, T. Tan, N. Zheng, X. Chen, and Y. Zhang, Eds. Cham: Springer International Publishing, 2019, pp. 528-538
  16. V. Zilvan, A. Ramdan, A. Heryana, D. Krisnandi, E. Suryawati, R. S. Yuwana, R. B. S. Kusumo, and H. F. Pardede, “Convolutional variational autoencoder-based feature learning for automatic tea clone recognition,” Journal of King Saud University - Computer and Information Sciences, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1319157821000227
  17. Y. Bengio, A. C. Courville, and P. Vincent, “Unsupervised feature learning and deep learning: A review and new perspectives,” CoRR, vol. abs/1206.5538, 2012. [Online]. Available: http://arxiv.org/abs/1206.5538
  18. V. Zilvan, A. Ramdan, E. Suryawati, R. B. S. Kusumo, D. Krisnandi, and H. F. Pardede, “Denoising convolutional variational autoencoders-based feature learning for automatic detection of plant diseases,” in 2019 3rd International Conference on Informatics and Computational Sciences (ICICoS), 2019, pp. 1–6
  19. A. Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks,” 2015
  20. M. A. Ranzato, Y.-L. Boureau, and Y. LeCun, "Sparse feature learning for deep belief networks," in Proceedings of the 20th International Conference on Neural Information Processing Systems, ser. NIPS'07. Red Hook, NY, USA: Curran Associates Inc., 2007, p. 1185-1192
  21. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, May 2015. [Online]. Available: https://doi.org/10.1038/nature14539
  22. S. Rahman, M. M. Rahman, M. Abdullah-Al-Wadud, G. D. Al-Quaderi, and M. Shoyaib, “An adaptive gamma correction for image enhancement,” EURASIP Journal on Image and Video Processing, vol.2016, no. 1, p. 35, 2016. [Online]. Available: https://doi.org/10.1186/s13640-016-0138-1
  23. N. Alswaidan and M. E. B. Menai, “Hybrid feature model for emotion recognition in arabic text,” IEEE Access, vol. 8, pp. 37 843–37 854, 2020
  24. M. Sohaib, C.-H. Kim, and J.-M. Kim, “A hybrid feature model and deep-learning-based bearing fault diagnosis,” Sensors, vol. 17, no. 12, p. 2876, Dec 2017. [Online]. Available: http://dx.doi.org/10.3390/s17122876
  25. D. Guo, M. Zhong, H. Ji, Y. Liu, and R. Yang, “A hybrid feature model and deep learning based fault diagnosis for unmanned aerial vehicle sensors,” Neurocomputing, vol. 319, pp. 155 – 163, 2018. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0925231218309962
  26. H. Farid, “Blind inverse gamma correction,” IEEE Transactions on Image Processing, vol. 10, no. 10, pp. 1428–1433, 2001
  27. P.Wang, F. Liu, C. Yang, and X. Luo, “Blind forensics of image gamma transformation and its application in splicing detection,” Journal of Visual Communication and Image Representation, vol. 55, pp. 80 – 90, 2018. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1047320318301159
  28. S. Dash and M. R. Senapati, "Enhancing detection of retinal blood vessels by combined approach of dwt, tyler coye and gamma correction," Biomedical Signal Processing and Control, vol. 57, p. 101740, 2020. [Online].Available: http://www.sciencedirect.com/science/article/pii/S1746809419303210
  29. A. Bhandari, A. Kumar, and G. Singh, "Improved knee transfer function and gamma correction based method for contrast and brightness enhancement of satellite image," AEU - International Journal of Electronics and Communications, vol. 69, no. 2, pp. 579 - 589, 2015. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S143484111400332X
  30. G. Cao, L. Huang, H. Tian, X. Huang, Y.Wang, and R. Zhi, “Contrast enhancement of brightness-distorted images by improved adaptive gamma correction,” Computers and Electrical Engineering, vol. 66, pp. 569 – 582, 2018. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0045790616306292
  31. Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Prentice Hall, Upper Saddle River, N.J. (2008). http://www.amazon.com/Digital-Image-Processing-3rd-Edition/dp/013168728X
  32. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv 1409.1556 (2014)
  33. Hughes, D.P., Salath, M.: An open access repository of images on plant health to enable the development of mobile disease diagnostics through machine learning and crowdsourcing. CoRR abs/1511.08060 (2015). 1511.08060

Last update:

No citation recorded.

Last update: 2024-11-05 07:17:15

No citation recorded.