Feature Extraction Based on Dimension Reduction and Clustering for Maize Leaf Spot Images

Image recognition and feature extraction play an important role in precision agriculture. In this paper, a manifold learning algorithm was used for dimension reduction of gray and RGB color images. To clarify the boundaries of disease spots and leaf background, three clustering algorithms were applied in experiments to realize clearer maize leaf disease images. Locally, linear embedding (LLE) and Gustafson–Kessel (GK) algorithms were selected to realize image feature extraction. It was shown that the recognition rate of feature extraction for gray and color images were 95% and 99%, respectively.

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