. 2010, 28(23): 81-85.
This paper proposes a plant automatic recognition method based on the leaf images, to be used in the computer-based automatic recognition system of the plant taxonomy. The color image is first transformed into a grey-level and a binary image in pre-processing including brightness correction, median filter and threshold segmentation. Six relative shape parameters and 5 texture parameters are then calculated, respectively, from the binary image and the grey-level image. The 6 shape parameters are the eccentricity, roundness, roundness index, direction angle, width and length ratio of the smallest rectangle that can cover the leaf, and short and long axes ratio of the best ellipse that can cover the leaf; and the five texture parameters are the second moment, contrast, correlation, entropy, moment deficit. Finally, an automatic recognition classifier based on RBF neural network is designed to determine which type of plant the leaf is from with the image of the leaf being used as a sample. Then, 60 images of leaf from 3 plants are used as samples to test the performance of the automatic recognition classifier. The average correct recognition rate reaches 70.83% when only the 6 shape parameters are used as the input data of the classifier, and it reaches 83.33% when both shape parameters and texture parameters are used as the input data of the classifier. The results show that the texture features can improve the average correct rate, and that the plant automatic recognition based on the leaf images is feasible.
