Mushroom Toxicity Recognition Based on Multigrained Cascade Forest
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 re...
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doaj-e15299dc59344f8d846573333d6026fa2021-07-02T12:44:33ZengHindawi LimitedScientific Programming1058-92441875-919X2020-01-01202010.1155/2020/88490118849011Mushroom Toxicity Recognition Based on Multigrained Cascade ForestYingying Wang0Jixiang Du1Hongbo Zhang2Xiuhong Yang3Fujian Key Laboratory of Big Data Intelligence and Security, Huaqiao University, Xiamen 361021, ChinaFujian Key Laboratory of Big Data Intelligence and Security, Huaqiao University, Xiamen 361021, ChinaFujian Key Laboratory of Big Data Intelligence and Security, Huaqiao University, Xiamen 361021, ChinaFujian Key Laboratory of Big Data Intelligence and Security, Huaqiao University, Xiamen 361021, ChinaDue 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.http://dx.doi.org/10.1155/2020/8849011 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yingying Wang Jixiang Du Hongbo Zhang Xiuhong Yang |
spellingShingle |
Yingying Wang Jixiang Du Hongbo Zhang Xiuhong Yang Mushroom Toxicity Recognition Based on Multigrained Cascade Forest Scientific Programming |
author_facet |
Yingying Wang Jixiang Du Hongbo Zhang Xiuhong Yang |
author_sort |
Yingying Wang |
title |
Mushroom Toxicity Recognition Based on Multigrained Cascade Forest |
title_short |
Mushroom Toxicity Recognition Based on Multigrained Cascade Forest |
title_full |
Mushroom Toxicity Recognition Based on Multigrained Cascade Forest |
title_fullStr |
Mushroom Toxicity Recognition Based on Multigrained Cascade Forest |
title_full_unstemmed |
Mushroom Toxicity Recognition Based on Multigrained Cascade Forest |
title_sort |
mushroom toxicity recognition based on multigrained cascade forest |
publisher |
Hindawi Limited |
series |
Scientific Programming |
issn |
1058-9244 1875-919X |
publishDate |
2020-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2020/8849011 |
work_keys_str_mv |
AT yingyingwang mushroomtoxicityrecognitionbasedonmultigrainedcascadeforest AT jixiangdu mushroomtoxicityrecognitionbasedonmultigrainedcascadeforest AT hongbozhang mushroomtoxicityrecognitionbasedonmultigrainedcascadeforest AT xiuhongyang mushroomtoxicityrecognitionbasedonmultigrainedcascadeforest |
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