Classification of Potato External Quality based on SVM and PCA

doi:10.23940/ijpe.17.04.p14.469478

Abstract

Abstract:

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.

Submitted on February 3, 2017; Revised on May 2, 2017; Accepted on June 8, 2017
References: 17

Juntao Xiong, Linyue Tang, Zhiliang He, Jingzi He, Zhen Liu, Rui Lin, and Jing Xiang. Classification of Potato External Quality based on SVM and PCA [J]. Int J Performability Eng, 2017, 13(4): 469-478.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References 0

	1. Al-Mallahi A., Kataoka T and Okamoto. Detection of potato tubers using an ultraviolet imaging-based machine vision system. Biosystems Engineering. (2010), 105(2): 257~265.
	2. Barnes M and Duckett T. Visual detection of blemishes in potatoes using minimalist boosted classifiers. Journal of Food Engineering, (2010), 98(3): 339~346
	3. Gao Xuwei. Feature Extraction Method Based on KPCA and Its Application. Nanjing: Nanjing University of Aeronautics and Astronautics, (2009).
	4. Hassankhani R, Navid H and Seyedarabi H. Potato surface defect detection in machine vision system. African Journal of Agricultural Research, (2012), 7(5): 844~850.
	5. Li Jiangbo, Rao Xiuqin and Ying Yibin. Detection of navel surface defects based on illumination-reflectance model. Transactions of the Chinese Society of Agricultural Engineering, (2011), 27(7): 338~342.
	6. Li Jinwei, Liao Guiping and Jin Jing. Method of potato external defects detection based on fast gray intercept threshold segmentation algorithm and ten-color model. Transactions of the Chinese Society of Agricultural Engineering, (2010), 26(10): 236~242.
	7. Liu He and Wang Maohua. Method for classification of apple surface defect based on digital image processing. Transactions of the Chinese Society of Agricultural Engineering, (2004), 20(6): 138~140..
	8. Liu Xiaotong. Study on Data Normalization in BP Neural Network. MECHANICAL ENGINEERING &AUTOMATION, (2010), 03: 122~126.
	9. Navid Razmjooy, Somayeh Mousavi and Soleymani. A real-time mathematical computer method for potato inspection using machine vision. Computers & Mathematics with Applications, (2012), 63: 268~279.
	10. Song Hui, Xue Yun and Zhang Liang-jun. Research on Kernel Function Selection Simulation Based on SVM Classification. JISUANJI YU XIANDAIHUA, (2011), 08: 133~136
	11. Song Yihuan, Rao Xiuqin and Ying Yibin, “Apple stem/calyx and defect discrimination using DT-CWT and LS-SVM,” Transactions of the Chinese Society of Agricultural Engineering, (2012), 28(9): 114~118.
	12. Yang Dongfeng and Chen Zhengguang. Greened Surface Detection of Potatoes Based on Color Character. Journal of Heilongjiang Bayi Agricultural University, (2011), 23(1): 83~87.
	13. Yang Hai. Research and Application on SVM Kernel Parameters Optimization. Hangzhou: Zhejiang University, (2014).
	14. Yu Xiaojuan, Liao Guiping and Li Jinwei. Greened potatoes detection based on hue threshold division. Transactions of the Chinese Society of Agricultural Engineering, (2009), 25(Supp.2): 314~319.
	15. Zhan Hui, Li Xiaoyu and Wang Wei. Determination of chestnuts grading based on machine vision. Transactions of the Chinese Society of Agricultural Engineering, (2010), 26(4): 327~331.
	16. Zhou Zhu, Huang Yi and Li Xiaoyu. Automatic detecting and grading method of potatoes based on machine vision. Transactions of the Chinese Society of Agricultural Engineering, (2012), 28(7): 178~183.
	17. Zhu Aihong, Zhao Shuai and Mao Mingliang. Research for Classification System of projects text based on LIB-SVM. Microcomputer Information, (2011), 27(4)