Abstract
Using CCD camera to dynamically collect real-time images of two sides of the apple, a flood filling + adaptive Ostu threshold segmentation algorithm is proposed to extract the outline of the apple. The minimum outer circle method is used to process the upper surface image of the apple to obtain the fruit diameter of the apple. Rectangular method is used to extract the apple's fruit shape features by processing the apple's side surface image; the image is converted from RGB to HSV space to extract the apple's coloring degree, fruit rust, and scar features, and the classification of the SVM decision tree based on the improved particle swarm algorithm Method for grading apples. The experimental results show that the recognition accuracy rates of extra-grade fruits, first-grade fruits, second-grade fruits and other outer fruits have reached 96%, 94%, 98% and 98%, respectively, and the classification rate has reached 4 s 1, which can satisfy the requirement for online-grading apples.
Publication Date
2-18-2023
First Page
123
Last Page
128, 153
DOI
10.13652/j.issn.1003-5788.2020.08.022
Recommended Citation
Qi, LI and Jia-kun, HU
(2023)
"Research on apple online classification based on machine vision,"
Food and Machinery: Vol. 36:
Iss.
8, Article 22.
DOI: 10.13652/j.issn.1003-5788.2020.08.022
Available at:
https://www.ifoodmm.cn/journal/vol36/iss8/22
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