A Study of Spore Image Classification Based on Feature Extraction

Abstract

Fungal diseases are the major diseases of agricultural production, and have brought tremendous impact to it. Identification of spore morphology plays an important role in the identification of fungi. This paper uses the microscopy images of two kinds of fungal spore and utilizes the technology of image analysis and recognition to classify them. We firstly get the underlying feature descriptors of these two kinds of microscopy images by RGB SIFT (Scale Invariant Feature Transform), then create the visual word dictionary using K-means clustering algorithm, at finally we use LDA, KNN and SVM to classify these two kinds of images. The results indicate that the classification of spore image based on feature extraction is feasible. In our future work, we will conduct the classification of related species and highly similar spore images.

Development of automatic counting system for urediospores of wheat stripe rust based on image processing

“…It has been used in plant disease diagnosis [10][11][12][13][14][15][16][17][18][19][20] , automatic grading of plant diseases [21][22][23][24][25] , and identification and automatic counting of plant pathogens [26][27][28][29][30] . And it is increasingly used in automatic counting of insects, cells and pathogenic microorganisms, etc.…”