Abstract
Multi-kernel dictionary learning for maize varieties classification
Highlights
Maize is one of the main grain crops in the world, and it is the main grain and economic crop in China
The iterative algorithm for consistent learning of dictionary matrix and multi-kernel[20,21,22] functions is proposed under sparse representation framework in reference[19]
The reconstruction error of sparse coded data is minimized by optimizing the sum of basis functions weighted by a set of multi-kernel functions
Summary
Maize is one of the main grain crops in the world, and it is the main grain and economic crop in China. The classification of maize mainly depends on manual evaluation of its shape, color and other aspects. It has the disadvantages of strong subjectivity and low efficiency, which increases the uncertainty of maize varieties classification. Machine vision instead of manual identification has the following advantages: (1) Multi-parameters measurement, comprehensive evaluation and classification; (2) Reduce human subjective factors and realize classification automatically; (3) Reduce inspecting error and improve accuracy. Thinking about grain surface defects[1,2,3], color difference, size difference, roughness and texture difference, computing by machine vision intelligent algorithms, by which the classification effect is achieved and human subjective factors are eliminated successfully.
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