{'fraction_non_alphanumeric': 0.020246478873239437, 'fraction_numerical': 0.0035211267605633804, 'mean_word_length': 5.145945945945946, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Leaf Recognition Method Based on Integration of Multi-migration Model

Plants are important to humans, and the plant classification based on leaf images has also attracted more and more attention. As a concrete implementation of deep learning in the field of image recognition, convolutional neural network has achieved many satisfactory results. In this paper, in order to solve the problem that the model recognition accuracy is not high due to the rare sample size, we integrate multiple migration models to recognize and classify the leaf images. A total of three models were selected, Squeezenet, Googlenet and Resnet for transfer learning and integration. We first pre-trained three networks on the Imagenet dataset. Then, the three models were fine-tuned on the LZU blade dataset. Finally, the three models are integrated by an integration strategy based on the voting results and prediction probabilities of each model. Through experiments, our leaf recognition method based on migration model has achieved 99.49% recognition accuracy on LZU blade dataset, which has certain advantages compared with other methods or networks.