การคัดแยกความสุกสตรอเบอรี่ด้วยซัพพอร์ตเวกเตอร์แมชชีน Strawberry Ripeness Classification by Support Vector Machine
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Published:
Jan 9, 2018
Keywords:
การคัดแยกสตรอเบอรี่ ซัพพอร์ตเวกเตอร์แมชชีน ฮีสโตแกรมแบบจำลองสี HSV ripeness strawberry classification support vector machine HSV Color Model Histogram
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Abstract
This article presents strawberry ripeness classification by support vector machine (SVM). Three groups strawberry including over ripeness, ripeness and un-ripeness are determined. Strawberry image is captured using low-resolution camera of 640 x 480 pixels. The histogram graph of HSV color model is used for feature extraction. To train the SVM, 150 strawberry samples are employed and 75 samples are used for testing. The result shows that the average accuracy of the SVM prediction of 97.3% is achieved. Therefore, this proposed system is suitable to use and develop for agriculture.
Article Details
How to Cite
เงินมูล เ., เหมยคำ พ., ปงลังกา ว., & ทิพจร ว. (2018). การคัดแยกความสุกสตรอเบอรี่ด้วยซัพพอร์ตเวกเตอร์แมชชีน: Strawberry Ripeness Classification by Support Vector Machine. Naresuan University Engineering Journal, 12(2), 55–62. Retrieved from https://ph01.tci-thaijo.org/index.php/nuej/article/view/78636
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Research Paper
References
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[11] Chira Kaewsuwan. (2006). Image Orientation Detection and Correction Using Support Vector Machine. Thesis Computer Technology. King Mongkut’s Institute of Technology North Bangkok.
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[2] Kongkrit Inthasan. strawberry. Technical Documents. Kanchanaburi Highland Agricultural Extention Center.
[3] Natthaphong Chalermtamrong, Narongchai Phocharoen andParichat Sermwuthisarn.( 2013). Automatic Sweet Pepper Separator. Thesis Faculty of Engineering, Kasetsart University Kamphaeng Saen.
[4] Vahid Mohammadi, Kamran Kheiralipour and Mahdi Ghasemi-Varnamkhasti. (2015) . Detecting maturity of persimmon fruit based on image processingtechnique. Scientia Horticulturae 184. 123–128.
[5] Chu zhang, Chentong Guo, Fei Liu, Wenwen Kong, Yong He and Binggan Lou. (2016). Hyperspectral imaging analysis for ripeness evaluation of strawberry with support vector machine. Journal of Food Engineering 179.11-18.
[6] Liu C, Liu W, Lu X, Ma F, Chen W, Yang J and Zheng L. (2014). Application of Multispectral Imaging to Determine Quality Attributes and Ripeness Stage in Strawberry Fruit. PLoS ONE 9(2): e87818. doi:10.1371/journal.pone.0087818.
[7] Gamal ElMasry, Ning Wang, Adel ElSayed and Michael Ngadi. (2007). Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry. Journal of Food Engineering 81. 98–107.
[8] Xu Liming and Zhao Yanchao. (2010). Automated strawberry grading system based on image processing. Computers and Electronics in Agriculture 71S. S32–S39.
[9] Xu Liming and Zhang Tiezhong. (2007). Influence of light intensity on extracted colour feature values of different maturity in strawberry. New Zealand Journal of Agricultural Research. Vol. 50: 559-565.
[10] Pornpon Thamrongrat. (2009). Web Page Classification Using Feature Reduction and Multi-Class SVM. Thesis Computer Science Prince of Songkla University.
[11] Chira Kaewsuwan. (2006). Image Orientation Detection and Correction Using Support Vector Machine. Thesis Computer Technology. King Mongkut’s Institute of Technology North Bangkok.
[12] Knerr, S., Personnaz, L., and Dreyfus, G. (1990). Single-layer learning revisited: A stepwise procedure for building and training neural network. Neurocomputing: Algorithms, Architectures and Applications, NATO ASI, Berlin: Springer-Verlag.
[13] Paingruthai Nusawat. (2013). Battery discharge rate prediction model for mobile phone using data mining. Thesis Computer and Information Science. Silpakorn University.
[14] Courant, R., Hilbert, D. (1937). Methods of mathematical physics. New York: Interscience Publishers,INC.