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| Diagnosis of rape nutrient deficiency basedon support vector machine |
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| Abstract: |
| Aimed at rape nutrient deficiency，we applied support vector machine in rape nutrient deficiency
diagnosis. Firstly，we determined the feature value for support vector machine classification，chose the RGB and HSV
color space as color features，and chose the mean and variance of energy，entropy，contrast，correlation as texture
features. Then，the support vector machine was applied to classify pattern recognition and was compared to BP neural
network. The results showed that the support vector machine was superior to BP in classification performance. Finally，
through genetic algorithm to optimize the parameters of support vector machine，the final classification accuracy was
improved. |
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