VGG16 for plant image classification with transfer learning and data augmentation

• Published in: International Journal of Engineering and Technology(UAE)
• Main Author: Abas M.A.H.; Ismail N.; Yassin A.I.M.; Taib M.N.
• Format: Article
• Language: English
• Published: Science Publishing Corporation Inc 2018
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054363713&doi=10.14419%2fijet.v7i4.11.20781&partnerID=40&md5=56bbc5f1af9f959f288f837cf76375ce

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 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. © 2018 Authors.