Seed Identification of Gramineous Grass Based on Difference of Local Fractal Dimensions

Abstract

This work proposes an approach based on the difference of local fractal dimension (DLFD) for seed identification of gramineous grass, rather than shape and color of the seeds. Being an important forge category of grassland, gramineous grass has been rarely investigated for the automated identification task by the researchers. Three main steps are involved in the extraction of DLFD. At first, the ROI image is equally divided into local blocks, and the fractal dimension of the partitions are calculated. Based on the average fractal dimension of all the blocks, the DLFD can then be obtained by subtracting the individual fractal dimension and the average, magnifying the contrast of the self-similarity of the images. Euclidean Distance and the nearest neighbor classifier are finally used for similarity measurement and classification. The novelty of the approach lies in applying fractal geometry in forage seed identification, a quite new area for pattern recognition. The experimental results demonstrate the effectiveness of the proposed method by some comparative analysis.