Deep Learning Based Approach for Classification of Mushrooms

Year 2023, , 487 - 498, 31.12.2023

Yağmur Demirel , Gözde Demirel

https://doi.org/10.54287/gujsa.1355751

Cited By: 1

Abstract

Deep learning algorithms have produced amazing results in recent years when used to identify items in digital photographs. A deep learning technique is suggested in this work to classify mushrooms in their natural habitat. The study's objective is to identify the most effective method for categorizing mushroom images produced by well-known CNN models. This study will be helpful for the field of pharmacology, mushroom hunters who gather mushrooms in the wild, and it will help to lower the number of people who are at risk of becoming ill from poisonous mushrooms. Images are taken from data labelled by INaturalist specialist. The photographs show mushrooms in their natural environment and feature a variety of backgrounds. The "Mobilenetv2_GAP_flatten_fc" model, which was the study's top performer, had a training data set accuracy of 99.99%. It was 97.20% accurate in the categorization that was done using the validation data. Using the test data set, the classification accuracy was 97.89%. This paper presents the results of a performance comparison between the best-performing model and a multitude of state-of-the-art models that have undergone prior training. Mobilenetv2_GAP_flatten_fc model greatly outperformed the trained models, according to the precision, recall, F1 Score. This illustrates how the basic training process of the suggested model can be applied to enhance feature extraction and learning.

Keywords

CNN, Image Processing, Mobilenetv2, Mushrooms

Ethical Statement

This study is an original study; the preparation, data collection, analysis and presentation of information in accordance with the principles and rules of scientific ethics that we act as a source for all data and information not obtained within the scope of this study. We declare that we have complied with ethical duties and responsibilities and that we have included these sources in the bibliography.

Thanks

We would like to thank and express our gratitude to our advisor, Assoc. Prof. Dr. Recep Eryiğit, who has always shared his knowledge and experience with us and devoted his valuable time to our questions.

References

• Chen, T. M., Rui, J., Wang, Q. P., Zhao, Z. Y., Cui, J. A., & Yin, L. (2020). A mathematical model for simulating the phase-based transmissibility of a novel coronavirus. Infectious Diseases of Poverty, 9(1), 24. https://www.doi.org/10.1186/s40249-020-00640-3
• Demirel, Y., & Demirel, G. (2023). Mushrooms. figshare. https://www.doi.org/10.6084/m9.figshare.24470113.v1
• Jarrett, K., Kavukcuoglu, K., Ranzato, M. A., & LeCun, Y. (2009, September 29 - October 2). What is the best multi-stage architecture for object recognition?. In: Proceedings of the International Conference on Computer Vision, (pp. 2146-2153). https://www.doi.org/10.1109/ICCV.2009.5459469
• Ketwongsa, W., Boonlue, S., & Kokaew, U. (2022). A New Deep Learning Model for the Classification of Poisonous and Edible Mushrooms Based on Improved AlexNet Convolutional Neural Network. Applied Sciences, 12(7), 3409. https://www.doi.org/10.3390/app12073409
• Krizhevsky, A., Sutskever, I., & Hinton, E. G. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Communications of the ACM, 60(6), 84-90. https://www.doi.org/10.1145/3065386
• Lee, H., Grosse, R., Ranganath, R., & Ng, A. Y. (2009, June 14-18). Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations. In: Proceedings of the 26th Annual International Conference on Machine Learning, (pp. 609-616). https://www.doi.org/10.1145/1553374.1553453
• Pinto, N., Doukhan, D., DiCarlo, J. J., & Cox., D. D. (2009). A high-throughput screening approach to discovering good forms of biologically inspired visual representation. PLOS Computational Biology, 5(11), e1000579. https://www.doi.org/10.1371/journal.pcbi.1000579
• Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L. C. (2018, June 18-23). MobileNetV2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, (pp. 4510-4520). https://www.doi.org/10.1109/CVPR.2018.00474
• Seidaliyeva, U., Akhmetov, D., Ilipbayeva, L., & Matson, E. T. (2020). Real-time and accurate drone detection in a video with a static background. Sensors (Basel), 20(14), 3856. https://www.doi.org/10.3390/s20143856
• Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information processing and management, 45(4), 427-437. https://www.doi.org/10.1016/j.ipm.2009.03.002
• Sutayco, M. J. Y., & Caya M. V. C. (2022, November 22-23). Identification of Medicinal Mushrooms using Computer Vision and Convolutional Neural Network. In: Proceedings of the 6th International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM), (pp. 167-171). https://www.doi.org/10.1109/ELTICOM57747.2022.10038007
• Turaga, S. C., Murray, J. F., Jain, V., Roth, F., Helmstaedter, M., Briggman, K., Denk, W., & Seung, H. S. (2010). Convolutional networks can learn to generate affinity graphs for image segmentation. Neural Computation, 22(2), 511-538. https://www.doi.org/10.1162/neco.2009.10-08-881
• Wang, B. (2022). Automatic Mushroom Species Classification Model for Foodborne Disease Prevention Based on Vision Transformer. Journal of Food Quality, 1173102. https://www.doi.org/10.1155/2022/1173102
• Zahan, N., Hasan, M. Z., Malek, M. A., & Reya, S. S. (2021, February 27-28). A Deep Learning-Based Approach for Edible, Inedible and Poisonous Mushroom Classification. In: Proceedings of the International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD), (pp. 440-444). https://www.doi.org/10.1109/ICICT4SD50815.2021.9396845
• Zhang, X., Han, L., Dong, Y., Shi, Y., Huang, W., Han, L., González Moreno, P., Ma, H., Ye, H., & Sobeih, T. (2019). A Deep Learning-Based Approach for Automated Yellow Rust Disease Detection from High-Resolution Hyperspectral UAV Images. Remote Sensing, 11, 1554. https://www.doi.org/10.3390/rs11131554
• Zhao, H., Ge, F., Yu, P., & Li, H. (2021). Identification of Wild Mushroom Based on Ensemble Learning. In: Proceedings of the IEEE 4th International Conference on Big Data and Artificial Intelligence (BDAI), (pp. 43-47). https://www.doi.org/10.1109/BDAI52447.2021.9515225
• Zheng, J. (2020). Sars-cov-2: an emerging coronavirus that causes a global threat. International Journal of Biological Sciences, 16(10), 1678, 1685. https://www.doi.org/10.7150/ijbs.45053