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Because of their abundance of amino acids, polysaccharides, and many othernutrients that benefit human beings, mushrooms are deservedly popular asdietary cuisine both worldwide and in China. However, if people eat poisonousfungi by mistake, they may suffer from nausea, vomiting, mental disorder, acuteanemia, or even death. Each year in China, there are around 8000 people becamesick, and 70 died as a result of eating toxic mushrooms by mistake. It iscounted that there are thousands of kinds of mushrooms among which only around900 types are edible, thus without specialized knowledge, the probability ofeating toxic mushrooms by mistake is very high. Most people deem that the onlycharacteristic of poisonous mushrooms is a bright colour, however, some kindsof them do not correspond to this trait. In order to prevent people from eatingthese poisonous mushrooms, we propose to use deep learning methods to indicatewhether a mushroom is toxic through analyzing hundreds of edible and toxicmushrooms smartphone pictures. We crowdsource a mushroom image dataset thatcontains 250 images of poisonous mushrooms and 200 images of edible mushrooms.The Convolutional Neural Network (CNN) is a specialized type of artificialneural networks that use a mathematical operation called convolution in placeof general matrix multiplication in at least one of their layers, which cangenerate a relatively precise result by analyzing a huge amount of images, andthus is very suitable for our research. The experimental results demonstratethat the proposed model has high credibility and can provide a decision-makingbasis for the selection of edible fungi, so as to reduce the morbidity andmortality caused by eating poisonous mushrooms. We also open source our handcollected mushroom image dataset so that peer researchers can also deploy theirown model to advance poisonous mushroom identification.

Using deep convolutional neural networks to classify poisonous and edible mushrooms found in China