Abstract: The classification of plants is mostly realized through the classification of plant leaves. In order to improve the accuracy of plant leaf classification, a plant leaf classification method based on multi feature fusion and extreme learning machine is proposed. Firstly, the color image of plant leaves is preprocessed to get the binary image and gray image in order to remove the color and background of leaves. Secondly, the shape feature and invariant moment feature of plant leaves are extracted from the binary image, and the gray level co-occurrence matrix parameter is extracted from the gray level image as the texture feature of leaves, so a total of 28 dimensional feature vectors are obtained. Finally, the classification strategy of the extreme learning machine is used to train and test the eigenvectors. Experiments on the open plant leaf dataset Flavia show that the accuracy of training classification is more than 99%, and the test accuracy is more than 98%. Experimental results show that this method can effectively improve the accuracy of plant leaf classification.

Key words: plant leaf classification, multi-feature fusion, extreme learning machine