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Recognition of edible mushrooms based on different image classification methods
- 1 Shanghai University of Engineering Science
* Author to whom correspondence should be addressed.
Abstract
The mushroom is one of the most common foods in nature, but there are many different types and many of them are deadly. The aim of this paper is to use computer vision methods to be able to provide a range of assistance to people on safari in identifying mushrooms and the future development of mushroom poisoning and non-poisoning identification. Considering the superior performance of convolutional neural networks in image recognition and classification, the advantages of multi-layer perceptrons and deep structure make it possible to achieve high recognition accuracy, three different CNN network models, GoogLeNet ResNet and SENet were chosen. They have been used to differentiate between toxic and non-poisonous mushrooms on 11,000 mushrooms. The tests showed that SENet had the greatest results, with a recognition accuracy of over 90%. With a recognition accuracy of around 65% each, GoogLeNet and ResNet placed second and third, respectively.
Keywords
mushroom, photo identification, GoogLeNet, ResNet, SENet, toxic mushrooms, image classification, computer vision
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Cite this article
Wu,Y. (2023). Recognition of edible mushrooms based on different image classification methods. Applied and Computational Engineering,27,185-193.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2023 International Conference on Software Engineering and Machine Learning
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