Leaf species recognition based on VGG16 networks and transfer learning

Abstract

Compared with other parts of the plant, leaves are easy to obtain and have obvious features. Therefore, leaf identification is also considered as one of the best methods for plant identification. For this reason, a transfer learning leaf recognition method based on VGG16 deep learning network is proposed. This method firstly carries out preprocess operations such as background whitening and standard normalization processing, then carries out data augmentation, then uses transfer learning to train VGG16 network model, and finally completes leaf recognition. The experimental results of Middle European Woody (MEW) plants data set and UCI Folio Leaf data set show that the recognition accuracy under a single background reaches 93.4% and 97.9%, respectively, which are significantly higher than traditional convolutional neural network and other recognition methods, effectively improving the recognition accuracy rate of plant leaves.