1. Machine Vision Detection Method for Peanut Mold Based on Improved HSV Space
- Author
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DING Can, WANG Wen-sheng, and HUANG Xiao-long
- Subjects
moldy peanuts ,machine vision ,hsv color space ,image processing ,bilateral filtering ,Food processing and manufacture ,TP368-456 ,Nutrition. Foods and food supply ,TX341-641 - Abstract
The aflatoxin produced by peanut mildew is highly carcinogenic, and it seriously affects food safety. In order to accurately and quickly identify moldy peanuts, this project proposes a detection method for moldy peanuts based on machine vision. Firstly, the peanut image was double-sided filtering and noise reduction, and then the image was converted to HSV space. The moldy peanut was recognized and detected by superimposing the mold color range extracted in H and S space and the open processing results of V space. The experimental results showed that the recognition accuracy of this method for moldy peanuts reached 95.3%, and the processing time for a single frame of peanut image was 0.6 seconds. Compared with other algorithms, this method had the advantages of fast speed and high accuracy, which can meet the real-time detection of moldy peanuts. At the same time, the grading processing of peanut mold is also more practical.
- Published
- 2024
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