Method for determining pear ring spot resistance based on support vector machine classification algorithm
A technology of support vector machine and classification algorithm, which is applied in the field of determination of pear ring disease resistance based on support vector machine classification algorithm, can solve problems such as wrong classification of pear tree resistance, and achieve the effect of scientific judgment and improvement of work efficiency.
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[0060] Embodiment 1, combining figure 1 :
[0061] S1: In this example, the leaves of 480 pear varieties were collected in the field and inoculated with ring spot. The ratio of training samples to testing samples is 2:1.
[0062] S2: Apply the Otsu segmentation threshold algorithm to calculate the optimal segmentation threshold of the binarized image, and use the optimal segmentation threshold to divide the image into the ring pattern lesion area and the non-lesion area;
[0063] S3: Image feature extraction of ring pattern lesions, including mean, standard deviation, kurtosis in statistical properties, and energy, correlation, contrast, and entropy in gray-scale co-occurrence matrix;
[0064] The eigenvectors for constructing ring-like lesions are:
[0065] v={MEAN,STD,KUR,ASM,COR,CON,ENTI}
[0066] In the formula: MEAN, STD, and KUR are the mean, standard deviation, and kurtosis of the gray histogram of the image, and ASM, COR, CON, and ENT are the energy, correlation, c...
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