VGG16 for Plant Image Classification with Transfer Learning and Data Augmentation

Mohamad Aqib Haqmi Abas, Nurlaila Ismail, Ahmad Ihsan Mohd Yassin, Mohd Nasir Taib
2018 International Journal of Engineering & Technology  
This paper discusses the potential of applying VGG16 model architecture for plant classification. Flower images are used instead of leaves as in other plant recognition model because the structure of shape and color of leaves are similar in nature. This might be disadvantageous when we want to use only leaves images as a sole feature of plants to classify the species. Previous work has demonstrated the effectiveness of using transfer learning, dropout and data augmentation as a method to reduce
more » ... overfitting problem of convolutional neural network model when applied in limited amount of images data. We have successfully build and train the VGG16 model with 2800 flower images. The model able to achieve a classification accuracy score of 96.25% for training set, 93.93% for validation set and 89.96% for testing set.
doi:10.14419/ijet.v7i4.11.20781 fatcat:mhibkwj6rraexbyw4ooqwdmi3a