Implementation of Deep Learning for Classification of MushroomUsing the CNN Algorithm

Implementation of Deep Learning for Classification of MushroomUsing the CNN Algorithm

Mushroom, Classification, Deep Learning, CNN

Mushroomsare a type of low-level plant that lacks chlorophyll. One of the advantages of fungiis that they are commonly utilized as food items in the community. This paper discussedthe implementation of CNN for the classification of mushrooms. The project aims to develop a robust system that can automate the labor-intensive task of mushroom classification. The CNN model will be trained on a large dataset of annotated mushroom images, learning to extract meaningfulfeatures and patterns for accurate categorization.To evaluate the performance of the developed system, a comprehensive set of metrics, including accuracy, precision, recall, and F1 score, will be used. The dataset will be split into training, validation, and testing sets to assess the model's generalization abilityto unseen data. Based on the experimental result, the average accuracy rate in the Agaricus Portobello test was % -99.89 %, % -99.89 % in the Amanita Phalloides test, % -99.59 % in the Cantharellus Cibarius test, % -98.89 % in the Gyromitra Esculenta test, % -99.96 % in the Hygrocybe Conica, and % -99.93 % in the Omphalotus Orealius.

Imam Mahfudz I’tisyam1, R. Nurhadi Wijaya2, Rike Pradila

https://ijicom.respati.ac.id/index.php/ijicom/article/view/42/43

August 2023

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