Research on detection method of sprouted potato based on SVM and weighted Euclidean distance

Shang Gao

doi:10.1117/12.2644666

10 November 2022 Research on detection method of sprouted potato based on SVM and weighted Euclidean distance

Shang Gao

Proceedings Volume 12301, 6th International Conference on Mechatronics and Intelligent Robotics (ICMIR2022); 123012D (2022) https://doi.org/10.1117/12.2644666
Event: 6th International Conference on Mechatronics and Intelligent Robotics, 2022, Kunming, China

Abstract

[Objective] According to the shortcomings of existing detection techniques, this paper proposes a new method based on SVM (Support Vector Machine) and weighted Euclidean distance detection to improve the detection accuracy of sprouted potatoes. [Method] Firstly, the original image of potato was obtained based on industrial camera, gray image and median filtering were used to ensure the image quality of the image, then B and H component training SVM classifiers were extracted in RGB color space and HSV color space respectively. After this, the well-trained SVM classifier was used to segment the potato image and background. Finally, the weighted Euclidean distance and morphology method were adopted to detect and mark the potato germination site. [Results] Under the help of the MatlabR2014a software platform, the weighted Euclidean distance and the traditional Euclidean distance method were employed to test the I and II potato samples. The experimental results reveal that the average recognition rate of the weighted Euclidean distance method is 90.6%, compared with 88.4% of the traditional Euclidean distance which indicates that the recognition rate of the method in this paper is higher, and the detection effect on the germinated potato is better.

Citation Download Citation

Shang Gao "Research on detection method of sprouted potato based on SVM and weighted Euclidean distance", Proc. SPIE 12301, 6th International Conference on Mechatronics and Intelligent Robotics (ICMIR2022), 123012D (10 November 2022); https://doi.org/10.1117/12.2644666

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KEYWORDS

Image segmentation

Image processing

RGB color model

FDA class I medical device development

FDA class II medical device development

Image acquisition

Cameras

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