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Mushroom Toxicity Recognition Based on Multigrained Cascade Forest
Joint Authors
Yang, Xiuhong
Wang, Yingying
Zhang, Hongbo
Du, Jixiang
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-01
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Due to the tastiness of mushroom, this edible fungus often appears in people’s daily meals.
Nevertheless, there are still various mushroom species that have not been identified.
Thus, the automatic identification of mushroom toxicity is of great value.
A number of methods are commonly employed to recognize mushroom toxicity, such as folk experience, chemical testing, animal experiments, and fungal classification, all of which cannot produce quick, accurate results and have a complicated cycle.
To solve these problems, in this paper, we proposed an automatic toxicity identification method based on visual features.
The proposed method regards toxicity identification as a binary classification problem.
First, intuitive and easily accessible appearance data, such as the cap shape and color of mushrooms, were taken as features.
Second, the missing data in any of the features were handled in two ways.
Finally, three pattern-recognition methods, including logistic regression, support vector machine, and multigrained cascade forest, were used to construct 3 different toxicity classifiers for mushrooms.
Compared with the logistic regression and support vector machine classifiers, the multigrained cascade forest classifier had better performance with an accuracy of approximately 98%, enhancing the possibility of preventing food poisoning.
These classifiers can recognize the toxicity of mushrooms—even that of some unknown species—according to their appearance features and important social and application value.
American Psychological Association (APA)
Wang, Yingying& Du, Jixiang& Zhang, Hongbo& Yang, Xiuhong. 2020. Mushroom Toxicity Recognition Based on Multigrained Cascade Forest. Scientific Programming،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1209211
Modern Language Association (MLA)
Wang, Yingying…[et al.]. Mushroom Toxicity Recognition Based on Multigrained Cascade Forest. Scientific Programming No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1209211
American Medical Association (AMA)
Wang, Yingying& Du, Jixiang& Zhang, Hongbo& Yang, Xiuhong. Mushroom Toxicity Recognition Based on Multigrained Cascade Forest. Scientific Programming. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1209211
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1209211