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A Performance Comparison of Mobile Net and VGG16 CNN Models in Plant Species Identification

Gargi Chandrababu, Ojus Thomas Lee, Rekha KS

Abstract


Plants are the building blocks of ecosystem and the major source of making ayurvedic medicine. Hence accurate identification of plant species is an important task. There are huge varieties of plant species which looks identical so it’s difficult to differentiate them. Finding the appropriate herb from thousands of herbs is an exhausting and time-consuming mission. Automated plant species identification systems help to resolve this difficulty to a large extent. Our project aims to develop automated plant identification system using two pre-trained CNN models namely MobileNet and VGG16. The developed models are compared based on classification accuracy, precision, recall, F1-score and cross entropy loss. Our studies have found that MobileNet outperforms VGG16 in plant identification task.

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