Seed Identification of Gramineous Grass Using Local Similarity Pattern and Linear Discriminant Analysis

Abstract

Subheading:Grass Seed Identification Using LSP and LDA. Background:Forage plays an important role in grassland in providing food for the livestock and keeping balance for the ecological system. Automated identification of fora-ge is an important task to improve the grassland management. Forage seed is the vital organ with relatively stable characteristics. Different from the relatively obvious varia-tions among the weeds, forage seeds are very similar in color, shape, size and texture. Especially, the resemblance of some seeds from different families makes the identification more difficult. Objective:In this paper, we proposed a seed identification approach based on local similarity pattern and linear discriminant analysis for gramineous grass, one of the main forge categories of the grassland, for a better identification performance. Method:The textural features derived from local similarity pattern and histogram statistics were input into linear discriminant analysis classifier, in which the former can extract more specific textures robust to noise and rotation variance, and the latter was more discriminative with classification information. Result:Experiments conducted on similar gramineous grass seeds of 12 species demonstrated the effectiveness of the algorithm, yielding an identification accuracy of 91.07%. Conclusion:Therefore, local similarity pattern and linear discriminant analysis classifier can well solve the identification problems of similar gramineous grass seeds.

Product Based Classification of Bulk Food Grains using Bag of Visual Words and Deep Features

“…Later, studies focus on hand crafted features such as Scale Invariant Feature Transform (SIFT) [12,13] and the histograms of gradient orientation (HOG) [14], which achieve high accuracy on controlled conditions for one seed on contrastive background.…”