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Classification of Potato External Quality based on SVM and PCA

Volume 13, Number 4, July 2017 - Paper 14 - pp. 469-478
DOI: 10.23940/ijpe.17.04.p14.469478

Juntao Xionga, Linyue Tanga, Zhiliang Hea, Jingzi Hea, Zhen Liua, Rui Lina, Jing Xiangb

aCollege of mathematics and informatics, South china agricultural university, Guangzhou, 510640
bHubei University for Nationalities, Enshi,445000

(Submitted on February 3, 2017; Revised on May 2, 2017; Accepted on June 8, 2017)


It is very important to classify and identify the quality of potato by computer vision. In order to realize the accurate and fast classification of potato, a classification and recognition method based on support vector machine and PCA is proposed. Study uses normal potato, green potato, germinated potato and damaged potato as the experiment sample. A total of 600 images were collected where 150 images of each sample was collected. The SVM multi classifier is designed to train the classifier based on the PCA principal component vector, and the key parameters of the classifier are optimized to improve the overall recognition rate of 96.6%. Separately, the normal potato recognition rate is 97.5%, the greened potatoes is 96.3%, the damaged potato is 95.0% and the germinated potato is 97.5%. The research results provide technical support for the intelligent grading of fruit and vegetable quality.


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