Abstract
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.
Highlights
Mushrooms are the fleshy fruiting bodies of certain fungus, some of which are edible, but a minority of them are toxic [1]
It is useful to identify whether a mushroom is poisonous according to the appearance features of the mushroom. e automatic recognition of mushroom toxicity has important social and application value in effectively preventing food poisoning [4]
Mushroom Dataset. e mushroom dataset provided by the University of California, Irvine was used to classify the toxicity of poisonous mushrooms [29]. e input features of the mushroom include class, cap shape, cap surface, cap color, bruises, odor, gill appendages, gill spacing, gill size, gill color, stalk shape, stalk root, stem-surface-above-ring, stemsurface-below-ring, stem-color-above-ring, stem-color-below-ring, veil type, veil color, ring number, ring type, spore print color, population, and habitat, for a total of 23 features. ese features, which can be observed directly, are classified with the feature calculation. ere are 8,124 recordings of mushroom data, which can be divided into two nearly balanced classes: poisonous (48%) and nonpoisonous (52%)
Summary
Mushrooms are the fleshy fruiting bodies of certain fungus, some of which are edible, but a minority of them are toxic [1]. A large number of people die [2, 3] from eating poisonous mushrooms. E automatic recognition of mushroom toxicity has important social and application value in effectively preventing food poisoning [4]. Current methods of recognizing poisonous mushrooms can be roughly divided into four categories: chemical determination, animal experimentation [5], fungal classification, and folk experience [6]. Erefore, the application of chemical determination methods to detect poisonous mushrooms is becoming increasingly popular [8]. Due to cumbersome handling and the great number of unstable toxins, the method of toxic chemical detection cannot be used to distinguish edible mushrooms from poisonous ones [9]. Due to cumbersome handling and the great number of unstable toxins, the method of toxic chemical detection cannot be used to distinguish edible mushrooms from poisonous ones [9]. is approach requires professional knowledge and is, not suitable for the average person
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