Analysis and Development of Eight Deep Learning Architectures for the Classification of Mushrooms

Analysis and Development of Eight Deep Learning Architectures for the Classification of Mushrooms

mushroom; deep learning; modified mobilenet; classification

One food item that is easy to find in nature is the mushroom. In terms of form and features, mushrooms are similar. Arranging mushrooms into groups so that poisonousand non-poisonous ones can be told apart is important. Real-time analysis of mushrooms is still not used very often. Previous studies focused primarily on performance and accuracy, ignoring architectural computing and a significant amount of data preprocessing. The used dataset is more laboratory-conditioned. This will impede the process of widespread implementation. The study suggests changes to eight current architectures: Modified DenseNet201, DenseNet121, VGG16,VGG19, ResNet50, InceptionNetV3, MobileNet, and EfficientNet B1. The development of this architecture took place within the areas of classification and hyperparameter learning. In contrast to the other eight architectures, the MobileNet architecture exhibits the lowest computational performance and highest accuracy, according to the comparison results. By employing the confusion matrix for evaluation, an accuracy of 82.7% is achieved. Modified MobileNet has the best speed because it keeps a lower-computation architecture and cuts down on unnecessary pre-processing. This means that a lot of people can use smartphones with more realistic data conditions to make it work

Lia Farokhah1, Suastika Yulia Riska

https://jurnal.iaii.or.id/index.php/RESTI/article/view/5498/902

Informatics, Technology and Design, Institut Teknologi dan Bisnis ASIA, Malang, Indonesi

19-02-2024

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