Classification of ripening stages of bananas based on support vector machine

Authors

  • Hou Juncai Northwest A&F University
  • Hu Yaohua 1. College of Mechanical and Electric Engineering, Northwest A&F University, Yangling 712100, China
  • Hou Lixia 1. College of Mechanical and Electric Engineering, Northwest A&F University, Yangling 712100, China
  • Guo Kangquan 1. College of Mechanical and Electric Engineering, Northwest A&F University, Yangling 712100, China
  • Takaaki Satake 2. Graduate School of Life and Environmental Sciences, University of Tsukuba, 1-1-1, Tennodai, Tsukuba 305-8702, Japan

DOI:

https://doi.org/10.25165/ijabe.v8i6.1275

Keywords:

banana, ripening stage, color change, support vector machine, classification, image recognition

Abstract

Non-destructive quality detection and automatic grading are important in fruit industry. The traditional way divides bananas into 7-level ripening stages based on color. This study investigated the changes of peel color at three positions of banana fingers, i.e. stalk, middle and tip. A support vector machine method was used to classify the ripening stages by color value L*, a* and b* as input data. The ripening stages were classified by 10-fold cross validation method of support vector machines with radial basis function kernel and linear function kernel. The results showed that the color change of middle position of banana finger adequately reflected the changes in banana ripening stages. a* value continuously increased from ripening stage 1 to ripening stage 7, L* and b* values increased from ripening stage 1 to ripening stage 5, and then decreased from ripening stage 5 to ripening stage 7. It was difficult to recognize the ripening stages using L*, a* and b* values individually. The accuracy of classification using support vector machine based on radial basis function kernel reached 96.5%, which was higher than that for linear function kernel. This research can provide a reference for automatic classification of banana ripening stages. Keywords: banana, ripening stage, color change, support vector machine, classification, image recognition DOI: 10.3965/j.ijabe.20150806.1275 Citation: Hou J C, Hu Y H, Hou L X, Guo K Q, Satake T. Classification of ripening stages of bananas based on support vector machine. Int J Agric & Biol Eng, 2015; 8(6): 99-103.

Author Biography

Hou Juncai, Northwest A&F University

College of Mechanical and Electric Engineering

References

Wang B, Yuan J, Zhang J, Shen Z, Zhang M, Li R. Effects of novel bioorganic fertilizer produced by Bacillus amyloliquefaciens W19 on antagonism of fusarium wilt of banana. Biology and Fertility of Soils, 2013; 49(4): 435−446.

Amorim E P, Santos-Serejo J A, Amorim V B O, Ferreira C F, Silva S O. Banana breeding at embrapa cassava and fruits. Acta Horticulturae, 2013; 986(18): 171−176.

FAOSTAT (2013). Food and Agriculture Organization of the United States. http://faostat.fao.org/site/567/ DesktopDefault.aspx?PageID=567#ancor. Accessed on [2014-02-12].

Pongprasert N, Srilaong V. A novel technique using 1-MCP microbubbles for delaying postharvest ripening of banana fruit. Postharvest Biology and Technology 2014, 95(9): 42–45.

Gomes J F S, Vieira R R, Leta F R. Colorimetric indicator for classification of bananas during ripening. Scientia Horticulturae, 2013; 150(4): 201–205.

Yang X, Song J, Fillmore S, Pang X, Zhang Z. Effect of high temperature on color, chlorophyll fluorescence and volatile biosynthesis in green-ripe banana fruit. Postharvest Biology and Technology, 2011; 62(3): 246–257.

Salvador A, Sanz T, Fiszman S M. Changes in color and texture and their relationship with eating quality during storage of two different dessert bananas. Postharvest Biology and Technology, 2007; 43(3): 319−325.

Von Loesecke H W. Bananas (2nd ed.). New York: Interscience. 1950; pp.52−66

Hou J, Hou L, Hu Y, Guo K, Satake T. Experimental study on ripening stage of bananas based on quartz crystal microbalance sensor detection. Transactions of the Chinese Society of Agricultureal Enggineering, 2014; 30(6): 256−262. (in Chinese with English abstract)

Quevedo R, Mendoza F, Aguilera J M, Aguilera J M, Chanona J, Gutiérrez-López G. Determination of senescent spotting in banana (Musa cavendish) using fractal texture fourier image. Journal of Food Engineering, 2008; 84(4): 509−515.

Du L, Yang X, Song Jun, Ma Z, Zhang Z, Pang X. Characterization of the stage dependency of high temperature ongreen ripening reveals a distinct chlorophyll degradation regulation inbanana fruit. Scientia Horticulturae, 2014; 180(12): 139–146.

Wu D, Sun D W, Color measurements by computer vision for food quality control–A review. Trends in Food Science & Technology, 2013; 29(1): 5−20.

Llobe E, Hine E L, Gardne J W, Franco S. Non-destructive banana ripeness determination using a neural network-based electronic nose. Measurement Science and Technology,

; 10(6): 538–548.

Wang J, Tang X J, Chen P S, Huang H H. Changes in resistant starch from two banana cultivars during postharvest storage. Food Chemistry, 2014; 156(1): 319–325.

Prinsi B, Negri A S, Fedeli C, Morgutti S, Negrini N, Cocucci M, et al. Peach fruit ripening: A proteomic comparative analysis of the mesocarp of two cultivars with different flesh firmness at two ripening stages. Phytochemistry, 2011; 72(10): 1251–1262.

Reyes M U, Paull R E, Williamson M R, Gautz L D. Ripeness determination of ‘Solo’ papaya (Carica papaya L.) by impact force. Applied Engineering in Agriculture, 1996; 12(6): 703–708.

Mikulic-Petkovsek M, Rescic J, Schmitzer V, Stampar F, Slatnar A, Koron D, et al. Changes in fruit quality parameters of four Ribes species during ripening. Food Chemistry, 2015; 173(2): 363–374.

Arefi A, Motlagh A M, Mollazade K, Teimourlou R F. Recognition and localization of ripen tomato based on machine vision. Australian Journal of Crop Science, 2011; 5(10): 1144−1149.

Wang Y, Cui Y, Chen S, Zhang P, Huang H, Huang G Q. Study on fruit quality measurement and evaluation based on color identification. Proceedings of SPIE, 2009; 7513: 75130F-1−75130F-6.

Xue H, Yang Q, Chen S. SVM: Support vector machines. In: Wu X, Kumar V (Ed.), editors. The top ten algorithms in data mining. Chapman & Hall, CRC, Chapter 3, London, Boca Raton, 2009; pp. 37−59.

Chen C R, Ramaswamy H S. Color and texture change kinetics in ripening bananas. Lebensmittel-Wissenschaft und-Technologie-food Science and Technology, 2002; 35(5): 415–419.

Burges C J C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 1998; 2(2): 121–167.

Zheng H, Lu H, Zheng Y, Chen C. Automatic sorting of Chinese jujube (Zizyphus jujuba Mill. cv. ‘hongxing’) using chlorophyll fluorescence and support vector machine. Journal of Food Engineering, 2010; 101(4): 402–408.

Lü Q, Cai J, Liu B, Deng L, Zhang Y. Identification of fruit and branch in natural scenes for citrus harvesting robot using machine vision and support vector machine. Int J Agric & Biol Eng, 2014; 7(2): 115−121.

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Published

2015-12-31

How to Cite

Juncai, H., Yaohua, H., Lixia, H., Kangquan, G., & Satake, T. (2015). Classification of ripening stages of bananas based on support vector machine. International Journal of Agricultural and Biological Engineering, 8(6), 99–103. https://doi.org/10.25165/ijabe.v8i6.1275

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Section

Agro-product and Food Processing Systems