Investigation of the Morphological and Color Changes of Damaged Green Plums During Storage Time Using Digital Image Processing Techniques

Year 2015, Volume: 28 Issue: 1, 133 - 139, 23.02.2015

Mahmut Sinecen , Riza Temizkan , Cengiz Caner

Abstract

The quality of green plums is directly affected by the uniformity of color and morphological characteristics which can be changed during harvest, handling, transportation and storage until they reach consumers. The determination of these characteristics is possible with image processing techniques. The advantages of this technique are being a simple, non-destructive and inexpensive method that does not require a complex apparatus. In this study, green plums are damaged at different rates (3 mm, 5 mm and 10 mm) by a texture analyzer and images are obtained at different storage periods. Non-damaged fruits were considered as control. Morphological and color features of these images are determined by using an image processing technique and the findings are compared among groups. In particular, the 10 mm damaging rate showed statistically significant differences for most of the morphological and color features (p<0.05). Therefore, the results showed that image processing technique is usable to classify the green plums according to damage rate.

Keywords

green plum, damaging, image processing

References

• Adel, K., & Diane, B. (2004). Classification, Composition of Fruits, and Postharvest Maintenance of Quality Processing Fruits: CRC Press.
• Adel, K., & Mikal, S. (2002). Atmosphere Modification Postharvest Physiology and Pathology of Vegetables: CRC Press.
• Blasco, J., Aleixos, N., Cubero, S., Gómez-Sanchís, J., & Moltó, E. (2009). Automatic sorting of satsuma (Citrus unshiu) segments using computer vision and morphological features. Computers and Electronics in Agriculture, 66(1), 1-8.
• Cubero, S., Aleixos, N., Moltó, E., Gómez-Sanchis, J., & Blasco, J. (2011). Erratum to: Advances in Machine Vision Applications for Automatic Inspection and Quality Evaluation of Fruits and Vegetables. Food and Bioprocess Technology, 4(5), 829-830.
• Diane, B., & Elisabeth, G. (2002). Preservative Treatments for Fresh-cut Fruits and Vegetables Fresh-Cut Fruits and Vegetables: CRC Press.
• Díaz-Pérez, J. C., Muy-Rangel, M. D., & Mascorro, A. G. (2007). Fruit size and stage of ripeness affect postharvest water loss in bell pepper fruit (Capsicum annuum L.). Journal of the Science of Food and Agriculture, 87(1), 68-73.
• Garrido-Novell, C., Pérez-Marin, D., Amigo, J. M., Fernández-Novales, J., Guerrero, J. E., & Garrido- Varo, A. (2012). Grading and color evolution of apples using RGB and hyperspectral imaging vision cameras. Journal of Food Engineering, 113(2), 281- 288.
• Gonzalez, R. C., & Woods, R. E. (2006). Digital Image Processing (3rd Edition): Prentice-Hall, Inc.
• Hu, J., Li, D., Duan, Q., Han, Y., Chen, G., & Si, X. (2012). Fish species classification by color, texture and multi-class support vector machine using computer vision. Computers and Electronics in Agriculture, 88(0), 133-140.
• Leiva-Valenzuela, G. A., & Aguilera, J. M. (2013). Automatic detection of orientation and diseases in blueberries using image analysis to improve their postharvest storage quality. Food Control, 33(1), 166-173.
• Luo, Z., Xie, J., Xu, T., & Zhang, L. (2009). Delay ripening of ‘Qingnai’ plum (Prunus salicina Lindl.) with 1-methylcyclopropene. Plant Science, 177(6), 705-709.
• Manganaris, G. A., Crisosto, C. H., Bremer, V., & Holcroft, D. (2008). Novel 1-methylcyclopropene immersion formulation extends shelf life of advanced maturity ‘Joanna Red’ plums (Prunus salicina Lindell). Postharvest Biology and Technology, 47(3), 429-433.
• Mendoza, F., Dejmek, P., & Aguilera, J. M. (2006). Calibrated color measurements of agricultural foods using image analysis. Postharvest Biology and Technology, 41(3), 285-295.
• Naderi-Boldaji, M., Fattahi, R., Ghasemi-Varnamkhasti, M., Tabatabaeefar, A., & Jannatizadeh, A. (2008). Models for predicting the mass of apricot fruits by geometrical attributes (cv. Shams, Nakhjavan, and Jahangiri). Scientia Horticulturae, 118(4), 293-298.
• Nanyam, Y., Choudhary, R., Gupta, L., & Paliwal, J. (2012). A decision-fusion strategy for fruit quality inspection using hyperspectral imaging. Biosystems Engineering, 111(1), 118-125.
• Navarro-Tarazaga, M. L., Massa, A., & Pérez-Gago, M. of hydroxypropyl methylcellulose-based edible film properties and postharvest quality of coated plums (Cv. Angeleno). LWT - Food Science and Technology, 44(10), 2328-2334. content on
• Paliwal, J., Visen, N. S., Jayas, D. S., & White, N. D. G. (2003). Cereal Grain and Dockage Identification using Machine Vision. Biosystems Engineering, 85(1), 51-57.
• Russ, J. C. (2011). The Image Processing Handbook, Sixth Edition: CRC Press, Inc.
• Tarhan, S. (2007). Selection of chemical and thermal pretreatment combination for plum drying at low and moderate drying air temperatures. Journal of Food Engineering, 79(1), 255-260.
• Valero, D., Martinez-Romero, D., Valverde, J. M., Guillén, F., & Serrano, M. (2003). Quality improvement and extension of shelf life by 1- methylcyclopropene in plum as affected by ripening stage at harvest. Innovative Food Science & Emerging Technologies, 4(3), 339-348.
• Van Zeebroeck, M., Ramon, H., De Baerdemaeker, J., Nicolaï, B., & Tijskens, E. (2007). Impact damage of apples during transport and handling. Postharvest Biology and Technology, 45(2), 157-167.
• Welch, C. R., Wu, Q., & Simon, J. E. (2008). Recent Advances Characterization. Curr Anal Chem, 4(2), 75-101. Analysis and
• Zagory, D., & Kader Adel, A. (1989). Quality Maintenance in Fresh Fruits and Vegetables by Controlled Atmospheres Quality Factors of Fruits and Vegetables (Vol. 405, pp. 174-188): American Chemical Society.