University of Bahrain
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Dynamic CNN combination for Morocco Aromatic and Medicinal Plant classification

Show simple item record Bahri, Abdelkhalak 2022-01-09T20:11:59Z 2022-01-09T20:11:59Z 2022-01-09
dc.identifier.issn 2210-142X
dc.description.abstract In few last years, deep learning has itself set up as the new strategy for plant classification. Deep learning has a best performance for object recognition. In this paper, we have focused on the case of Morocco aromatic and medicinal plant (AMP) classification. Leaf is an important organ of plant, it has shown satisfying performances for plant classification and recognition In addition to leaves, we have used an others organs of leaf veins and branches. We have proposed a new model combining dynamically the CNN classification result using the entropy impurity method. In the experiments, we have used VGG16, ResNet50 and Inception V3 CNN models. We have used Keras with Tensor flow backend to build and compile all neural network models. The experiments present that our model shown the higher classification accuracy. en_US
dc.language.iso en en_US
dc.publisher University of Bahrain en_US
dc.subject Deep learning en_US
dc.subject classification en_US
dc.subject combination en_US
dc.subject CNN en_US
dc.subject plants en_US
dc.title Dynamic CNN combination for Morocco Aromatic and Medicinal Plant classification en_US
dc.volume 11 en_US
dc.issue 1 en_US
dc.pagestart 239 en_US
dc.pageend 249 en_US
dc.contributor.authorcountry Morocco en_US
dc.contributor.authoraffiliation Ensah, UAE- University, en_US
dc.source.title International Journal of Computing and Digital Systems en_US
dc.abbreviatedsourcetitle IJCDS en_US

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