Characteristic angle cosine value method for detecting defects of honey peach brown rot

Abstract

The invention discloses a characteristic angle cosine value method for detecting defects of honey peach brown rot. The method comprises the following steps of: extracting images at the wave bands of 660nm, 680nm and 700nm from a honey peach hyperspectral image, and performing single threshold segmentation on the image at the wave band of 660nm to obtain a fruit area; performing 3*3 mean filtering on the fruit areas of the images at the three wave bands, and performing mean normalization on the spectrum; by taking a pixel point in the fruit area, taking a wavelength value as a horizontal coordinate, taking the spectrum normalization value as a vertical coordinate, taking an included angle ABC formed by three points of A (lambdaA, RA), B(lambdaB, RB) and C(lambdaC, RC) as a characteristic angle and taking a cosine value of the characteristic angle as a characteristic value, classifying pixels in the fruit areas of the images, and detecting the defects of the brown rot. The defects of honey peach brown rot are detected by using three wave bands only, the detection cost is reduced, the surface coloration interference of the honey peach is eliminated, and the method can be used for detecting the defects of apples and other fruits containing chlorophyll.

Description

technical field [0001] The invention relates to a fruit defect detection method, in particular to a characteristic angle cosine value method for detection of peach brown rot defect. Background technique [0002] Juicy peaches are not resistant to storage and transportation, and are prone to various mechanical damage and bacterial infection during storage and transportation, resulting in fruit corruption and economic losses to fruit farmers and consumers. Peaches, whether fresh, canned or juiced, are selected and graded before entering the market and being processed. Therefore, non-destructive detection of peach defects is necessary. [0003] Since the surface color of peaches can be divided into base color and coloring, it is more difficult to use RGB color machine vision system to detect peach defects, and the sample information detected by hyperspectral image technology combines image information and spectral information, which can Comprehensively reflect the external ch...

AI Technical Summary

Problems solved by technology

However, the number of bands involved in the first two digits is too large, which is not conducive to the realization of the multispectral online detection system.

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