Jitdumrong Preechasuk
ABSTRACT
Mushrooms are fungi. The edible mushrooms include nutritional content and health benefits. However, some mushroom species is toxic and contains poisonous substances that could cause illness and lead to death. Mushroom poisoning accounts for approximately 70% of natural poisoning and often causes death. However, there are only 30-50 poisonous species among the thousands of species found on earth, and of these, no more than 10 are fatally poisonous [1]. The main reason of eating poisonous mushrooms is the lack of knowledge and skill to classify the edible and poisonous mushrooms. Besides, the physical characteristics of mushrooms are similar. Therefore, this work focuses on the classification of 45 types of mushrooms. This work aims to reduce the number of illness persons whom are risk of exposure to toxic mushrooms.
This work proposes a new model of classifying 45 types of mushrooms including edible and poisonous mushrooms by using a technique of Convolution Neural Networks. The proposed model was tested on both types of mushroom. It was trained to construct the CNN models and used the trained models to classify all types of mushroom. The proposed model gives the results of 0.78, 0.73 and 0.74 of precision, recall and F1 score, respectively. It concluded that the proposed model can classify types of mushroom image with efficiently and effectively.
- B. S. Patowary, "Mushroom Poisoning-an overview" Journal of college of Medical Sciences-Nepal, vol. 6, pp. 56--61, 2010.Google Scholar
Cross Ref
- Bureau of Epidemiology, Department of Disease Control, and Ministry of Public Health. (October 25, 2019). Available: https://apps.boe.moph.go.th/boeeng/about_us.phpGoogle Scholar
- National Institue of Health of Thailand. Department of Medical Science. Minitary of Public Health. (October 25,2019). Toxic from poisonous mushrooms. Available: http://www.dmsc-library.moph.go.th/ebooks/files/showimgpic%20(23).pdfGoogle Scholar
- Forest Research Pathology Sub Group. Forest Entomology and Microbiology Section. Forest Research and Development Bureau. Royal Forest Department. (October 25, 2019). Poisonous mushroom. Available: http://forprod.forest.go.th/forprod/research_pathology/upload/file/docpdf4.pdfGoogle Scholar
- S. Ismail, A. R. Zainal, and A. Mustapha, "Behavioural features for mushroom classification," in 2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE), 2018, pp. 412--415.Google Scholar
- M. U. Ghani, A. Ö. Argunşah, I. Israely, D. Ünay, T. Taşdizen, and M. Çetin, "On comparison of manifold learning techniques for dendritic spine classification," in 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), 2016, pp. 339--342.Google Scholar
- A. A. Gowen, C. P. O. Donnell, J. M. Frias, and G. Downey, "Hyperspectral imaging for mushroom (agaricus bisporus) quality monitoring," in 2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009, pp. 1--4.Google Scholar
- A. Masoudian and K. A. Mcisaac, "Application of Support Vector Machine to Detect Microbial Spoilage of Mushrooms," in 2013 International Conference on Computer and Robot Vision, 2013, pp. 281--287.Google Scholar
- A. Anil and M. Arora, "Image Processing Based Non-Destructive Testing of Mushroom Sample," in 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2018, pp. 1625--1629.Google Scholar
- Munirah M. Y., Rozlini M, and Mariam S. Y., "An Expert System development: Its application on Diagnosing Oyster Mushroom Diseases," in 2013 13th International Conference on Control, Automation and Systems (ICCAS 2013), 2013, pp. 329--332. Psilocybe cubenesis. Available: https://en.wikipedia.org/wiki/Psilocybe_cubensisGoogle Scholar
- T. Hazisawa, M. Toda, T. Sakoil, K. Matumural, and M. Fukuda, "Image analysis method for grading raw shiitake mushrooms," in The 19th Korea-Japan Joint Workshop on Frontiers of Computer Vision, 2013, pp. 46--52.Google Scholar
- H. Yang and Y. He, "Nondestructive Variety Discrimination of Fragrant Mushrooms Based on Vis/NIR Spectral Analysis," in 2008 Congress on Image and Signal Processing, 2008, pp. 64--67.Google Scholar
- WIKIPEDIA The Free Encyclopedia. (October 25, 2019).Google Scholar
Index Terms
- Image Analysis of Mushroom Types Classification by Convolution Neural Networks
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