Automatic Leaf Recognition Based on Deep Semi-supervised Learning

Automatic leaf recognition algorithms have been widely used in the fields of plant teaching and ecological protection. However, due to the wide variety of plants and the small differences between classes, traditional deep learning methods require a large amount of data annotations to obtain better training results. Therefore, a semi-supervised deep learning method for plant leaf automatic identification is proposed. First, based on the idea of consistent regularization, both of explicit and implicit data perturbation processes are designed to improve the quality of data perturbation. Secondly, by utilizing a deep feature extraction network DenseNet, the ability of fine detail recognition can be effectively improved. Finally, based on the simulated annealing training strategy, the abnormal data in the training process is filtered to alleviate the over-fitting phenomenon. Extensive experiments are conducted on a public dataset MalayaKew-D3 with 5 284 leaf images and a private dataset LeafSZU-2021 with 18 000 leaf images. Compared with the fully supervised learning methods, the proposed method can still achieve 92.36% to 96.85% accuracy of plant leaf recognition by using only 30% to 50% labeled data. Compared with the latest semi-supervised spherical mean clustering model under the same amount of data annotations, the average recognition accuracy of the proposed method improves by 2.95%, where the number of model parameters also decreases by 38.12%, and the recognition speed improves by 61.51%.