A computer vision system for classification of some Euphorbia (Euphorbiaceae) seeds based on local binary patterns

In this study, a computer vision system was proposed for the seed images classification. The classification process was performed using uniform local binary patterns obtained from digital seed images. In this study, 240 (120 training and 120 test) images of the seed were used. First, the average uniform histograms of each type of seed (seed type classes) was obtained for the training set. Then the uniform LBP histogram of each seed in the test set were produced and compared with histograms of classes by using nearest neighbor. The Euclidean distance, sum square error, histogram intersection and Chi-square statistics were used to calculate the distance between seed samples. 95.83% of seed images has been diagnosed properly with the proposed. As a result, the surface shape of the seeds include important information patterns to determine the taxonomic relationships,it is is expected that the computer vision systems provide significant advantages to identify the type of seed.