Abstract: Plant leaf recognition is an important branch and hotspot of plant automatic classification and recognition.In order to improve the accuracy of plant leaf recognition,a method of multi-feature fusion combined with convolution neural network is proposed.In the experiment,Firstly,we extract LBP features and Gabor features.The leaf LBP coding map is evenly divided into 7×7 blocks,and different weights are assigned.The LBP histogram of each sub-block is calculated,and then normalized,and the normalized histogram of all sub-blocks is connected to obtain the whole Histogram and feature map.We use a four-direction Gabor filter to filtering the leaf image to obtain four sub-images.Each sub-image is divided into 4×4 sub-blocks,the average and variance of the filtered energy values of each sub-block are calculated,obtaining 128 dimensional Gabor features.Then the image of plant leaves is preprocessed to size 227×227 pixels with labels,local binary patterns features and Gabor features are extracted,the multi-features are added and fused through the feature fusion layer.Then the convolution neural network (AlexNet) framework is used as the classifier,and the full connection layer is used to identify the plant leaves.In order to avoid over fitting phenomenon,"dropout" method is used to train convolutional neural network,optimizing training model by adjusting learning rate and dropout value.The experimental results show that the plant recognition method based on multi-feature fusion convolution neural network classifies 32 kinds of leaves in Flavia leaves database and 189 kinds of leaves in MEW2014 leaves database,with an average correct recognition rate of 93.25% and 96.37%,respectively.This shows that compared with the general convolution neural network recognition method,this method can improve the recognition accuracy and robustness of plant leaves.

Key words: Convolutional neural network, Deep learning, Gabor, Leaf image recognition, Local binary patterns