Abstract
Objective: To realize the rapid on-line detection of defective rice flour. Methods: Non-contact data acquisition of rice flour blocks through cameras, image upload and processing, obtained the contour perimeter and area, approximate contour perimeter and area, approximate contour points and radius of the contour circle. According to the characteristics of rice flour block sample data, the SVM classification algorithm was used to analyze the sample set composed of multi feature data of rice flour block. Results: Compared with five algorithms, the average accuracy of GBDT classification algorithm was 89% with elapsed time of 1.10 s. The average accuracy of KNN classification algorithm was 88% with elapsed time of 0.23 s. The average accuracy of logistic regression classification algorithm was 88% with elapsed time of 0.68 s. The average accuracy of random forest classifica-tion algorithm was 87% with elapsed time of 0.47 s. The average accuracy of tree classification algorithm was 87% with elapsed time of 0.084 s. SVM classification algorithm had the highest average detection accuracy, up to 95%, and the shortest average elapsed time of 0.000 97 s. Conclusion: SVM classification algorithm has the characteristics of high accuracy and low elapsed time, which adapt the on-line detection of defective rice flour.
Publication Date
6-30-2022
First Page
78
Last Page
81,86
DOI
10.13652/j.spjx.1003.5788.2022.90151
Recommended Citation
Lu-min, TAN and Xin-gang, FENG
(2022)
"On line fast detection of defective rice flour based on machine learning algorithm,"
Food and Machinery: Vol. 38:
Iss.
5, Article 15.
DOI: 10.13652/j.spjx.1003.5788.2022.90151
Available at:
https://www.ifoodmm.cn/journal/vol38/iss5/15
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