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Fungus Classification Based on CNN Deep Learning Model

Year 2023, Volume: 12 Issue: 1, 226 - 241, 22.03.2023
https://doi.org/10.17798/bitlisfen.1225375

Abstract

Artificial intelligence has been developing day by day and has started to take a more prominent place in human life. As computer technologies advance, research on artificial intelligence has also increased in this direction. One of the main goals of this research is to examine how real problems in human life can be solved using artificial intelligence-based deep learning, and to present a case study. Poisoning from the consumption of poisonous fungi is a common problem worldwide. To prevent these poisonings, a mobile application has been developed using Convolutional Neural Networks (CNNs) and transfer learning to detect the species of fungus. The application informs the user about the type of fungus, whether it is poisonous or non-toxic, and whether it is safe to eat. The aim of this study is to reduce poisoning events caused by incorrect fungus detection and to facilitate the identification of fungus species. The developed deep learning model is integrated into a mobile application developed by Flutter that is a mobile application development framework, which enable the detection of fungus species from images taken from the camera or selected from the gallery. CNNs and the EfficientNetV2 model, a transfer learning method, were used. By using these two methods together, the classification accuracy rate for 77 fungus species was obtained as 97%.

References

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Year 2023, Volume: 12 Issue: 1, 226 - 241, 22.03.2023
https://doi.org/10.17798/bitlisfen.1225375

Abstract

References

  • [1] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.
  • [2] K. O’Shea and R. Nash, “An Introduction to Convolutional Neural Networks,” arXiv [cs.NE], 2015.
  • [3] F. Sultana, A. Sufian, and P. Dutta, “Advancements in image classification using convolutional Neural Network,” arXiv [cs.CV], 2019.
  • [4] L. Picek, M. Šulc, J. Matas, J. Heilmann-Clausen, T. S. Jeppesen, and E. Lind, “Automatic fungi recognition: Deep learning meets mycology,” Sensors (Basel), vol. 22, no. 2, p. 633, 2022.
  • [5] S. Sladojevic et al., Fungi Recognition: A Practical Use Case. 2020.
  • [6] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM, vol. 60, no. 6, pp. 84–90, 2017.
  • [7] K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification,” in 2015 IEEE International Conference on Computer Vision (ICCV), 2015.
  • [8] K. Kamnitsas et al., “Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation,” Med. Image Anal., vol. 36, pp. 61–78, 2017.
  • [9] N. Z. Kayalı and S. Ve Ilhan Omurca, Konvolüsyonel Sinir Ağları (CNN) ile Çin Sayı Örüntülerinin Sınıflandırması. 2021.
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  • [12] “What are Convolutional Neural Networks?” Ibm.com. [Online]. Available: https://www.ibm.com/topics/convolutional-neural-networks. [Accessed: 27-Dec-2022].
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There are 24 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Araştırma Makalesi
Authors

Serhat Oral 0009-0005-2761-1295

İrfan Ökten 0000-0001-9898-7859

Uğur Yüzgeç 0000-0002-5364-6265

Early Pub Date March 23, 2023
Publication Date March 22, 2023
Submission Date December 28, 2022
Acceptance Date March 3, 2023
Published in Issue Year 2023 Volume: 12 Issue: 1

Cite

IEEE S. Oral, İ. Ökten, and U. Yüzgeç, “Fungus Classification Based on CNN Deep Learning Model”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 12, no. 1, pp. 226–241, 2023, doi: 10.17798/bitlisfen.1225375.

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