Abstract: In view of the leaves of different species of plants in nature may have small differences, which leads to the problem of leaf recognition errors of some native plants and invasive plants with similar edge profiles, a PF-VGGNet model is proposed. The common VGGNet model performs well in image classification. Using the sequential connection structure, it can extract the high-level semantic information features of the image, but the shallow contour and texture features of some images also play a key role in the classification. The PF-VGGNet model can fuse the shallow contour and texture features with the deep semantic information of the network to realize the automatic recognition of plant leaves. The experimental results show that the PF-VGGNet model has better recognition effect than other algorithms on the self built data set of alien invasive plant leaves, and the accuracy rates in training set and test set are 99.89% and 99.63% respectively. The PF-VGGNet can effectively reduce the problem of recognition error caused by the similar edge contour of leaves, can quickly identify the leaves of alien invasive plants, and provide support for the prevention and control of alien plants.

Key words: plant leaf recognition, convolutional neural network, VGGNet model, pyramid feature input