Abstract
In existing machine vision technology for fruit defects, the hue appears different, and the defect area is small due to the irregularity of illumination reflection from the surface incident light source, this makes it difficult to extract the defect area. Thus, we proposed an apple defect detection method based on the Fuzzy C-means Algorithm and the Nonlinear Programming Genetic Algorithm (FCM-NPGA) in combination with a multivariate image analysis. First, the image was denoised and enhanced through fractional differentiation. The noise points and edge points were removed, and the important texture information was preserved. Then, the FCM-NPGA algorithm was used to segment the suspicious defect graph. Finally, a method based on a multivariate image analysis strategy was used to detect the flaws of the apple's suspicious defect map. The application results of 2000 images showed that the overall detection accuracy was 98%. Experiments show that the apple defect detection algorithm based on FCM and NPGA combined with multi-image analysis method is effective.
Highlights
At present, the technology after fruit picking for surface defect detection, grading, and sorting is relatively backward in China, resulting in the low cost of fruit processing, inferior fruit quality, poor sales, and low economic benefits [1]
In order to solve the above problems, we proposed a fruit surface defect detection method based on FCM-NPGA and a multiple image analysis strategy
The segmentation method based on the FCM-NPGA tested in this study was compared with the standard FCM method, and the results showed that the FCM-NPGA had better similarity with the original image
Summary
The technology after fruit picking for surface defect detection, grading, and sorting is relatively backward in China, resulting in the low cost of fruit processing, inferior fruit quality, poor sales, and low economic benefits [1]. Literature [10] studied Jonagold apples that had been damaged within the last two hours They applied the particle filter algorithm to the post-processing image and built a pixel-based PLS-DA model by improving the spectral signal-to-noise ratio. In the research of literature [15], the image of the apple region was obtained through morphological operations and hole filling; median filtering and edge detection algorithms were used to extract the shape, color, and defect features of the apple; the particle swarm optimization SVM was applied to classify apples, reaching the accuracy of 92%. In order to solve the above problems, we proposed a fruit surface defect detection method based on FCM-NPGA and a multiple image analysis strategy.
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