Loading...
Please wait, while we are loading the content...
Similar Documents
A New Deep Learning Model for the Classification of Poisonous and Edible Mushrooms Based on Improved AlexNet Convolutional Neural Network
| Content Provider | MDPI |
|---|---|
| Author | Ketwongsa, Wacharaphol Boonlue, Sophon Kokaew, Urachart |
| Copyright Year | 2022 |
| Description | The difficulty involved in distinguishing between edible and poisonous mushrooms stems from their similar appearances. In this study, we attempted to classify five common species of poisonous and edible mushrooms found in Thailand, Inocybe rimosa, Amanita phalloides, Amanita citrina, Russula delica, and Phaeogyroporus portentosus, using the convolutional neural network (CNN) and region convolutional neural network (R-CNN). This study was motivated by the yearly death toll from eating poisonous mushrooms in Thailand. In this research, a method for the classification of edible and poisonous mushrooms was proposed and the testing time and accuracy of three pretrained models, AlexNet, ResNet-50, and GoogLeNet, were compared. The proposed model was found to reduce the duration required for training and testing while retaining a high level of accuracy. In the mushroom classification experiments using CNN and R-CNN, the proposed model demonstrated accuracy levels of 98.50% and 95.50%, respectively. |
| Starting Page | 3409 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app12073409 |
| Journal | Applied Sciences |
| Issue Number | 7 |
| Volume Number | 12 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-03-27 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Microbiology Alexnet Convolutional Neural Network Deep Learning Googlenet Mushroom Resnet-50 |
| Content Type | Text |
| Resource Type | Article |
