|تعداد مشاهده مقاله||111,625,098|
|تعداد دریافت فایل اصل مقاله||86,246,309|
Application of Image Processing for Investigating the Effect of Nanozeolite and Nanosponge on Flesh Firmness of Cold Stored Cantaloupe
|International Journal of Horticultural Science and Technology|
|مقاله 11، دوره 4، شماره 1، شهریور 2017، صفحه 127-133 اصل مقاله (623.48 K)|
|نوع مقاله: Research paper|
|شناسه دیجیتال (DOI): 10.22059/ijhst.2017.228224.178|
|Navid Yazdani* 1؛ Behnam Osanloo1؛ Mahmoud Lotfi1؛ Keyvan Asefpour Vakilian2|
|1Department of Horticulture, College of Aburaihan,University of Tehran, Pakdasht, Tehran, Iran.|
|2Department of Biosystems Engineering, College of Aburaihan,University of Tehran, Pakdasht, Tehran, Iran|
|Digital image processing is an emerging tool to predict fruit quality; therefore present study was carried out to develop an image processing technique for investigating the storage life of cantaloupe. Potassium permanganate (KMnO4) impregnated materials were used to prolong the postharvest life of cantaloupe fruit and the effects of these treatments were evaluated by 3 image textural features parameters and flesh firmness. The treatments were divided into seven groups containing untreated, conventional paper impregnated with 7% KMnO4, nanozeolite impregnated with 7% KMnO4 and nanosponge impregnated with 0, 4, 7 and 10% KMnO4 respectively in packages. Findings of the investigations showed that the nanosponges impregnated by 7 or 10% KMnO4 could preserve the quality of cantaloupe over time by maintaining its color and flesh firmness which could be a result of ethylene absorption. Nanozeolite covered with 7% KMnO4 was also a good compound to preserve the fruit firmness. Image processing features including Entropy was increased and Homogeneity was decreased during cold storage whereas, fruits that are treated with nanosponge impregnated with 10% KMnO4 showed less Entropy and more Homogeneity than other treatments. Moreover, all KMnO4 treated fruits had better values of flesh firmness and image textural parameters than control. A significant correlation was observed between flesh firmness and image parameters. In total, nano-materials showed acceptable performance in extending the postharvest life of cantaloupes based on the fruit firmness and our findings illustrated that the image processing technique can be used to assess the quality of cantaloupe fruits during storage.|
|Image processing؛ Melon (Cucumis melo L.)؛ Nanosponge؛ Nanozeolite؛ Phytomonitoring|
Asefpour Vakilian K, Massah J. 2012. Non‐linear growth modeling of greenhouse crops with image textural features analysis. International Research Journal of Applied and Basic Sciences 3, 197-202.
Asefpour Vakilian K, Massah J. 2013. Performance evaluation of a machine vision system for insect pests identification of field crops using artificial neural networks. Archives of Phytopathology and Plant Protection 46, 1262-1269.
Corkidi G, Balderas-Ruiz K.A, Taboada B, Serrano-Carreon L, Galindo E. 2006. Assessing mango anthracnose using a new three-dimensional image-analysis technique to quantify lesions on fruit. Plant Pathology 55, 250-257.
Gao X, Tan J. 1996. Analysis of expended-food texture by image processing part I: Geometric properties. Journal of Food Process Engineering 19, 425-444.
Haralick R.M, Shanmugam K, Dinstein I. 1973. Textural features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics 3, 610-621.
Huang Y, Lan Y, Thomson S.J, Fang A, Hoffmann W.C, Lacey R.E. 2010. Development of soft computing and applications in agricultural and biological engineering. Computers and Electronics in Agriculture 71, 107-127.
Jain R, Kasturi R, Schunck B.G. (ed.). 1995. Machine Vision, McGraw-Hill, New York.
Kavdir I, Guyer D.E. 2002. Apple sorting using artificial neural networks and spectral imaging. T. ASABE 45, 1995-2005.
Khosravi F, Khosravi M, Pourseyedi E. 2015. Effect of nanozeolite and potassium permanganate on shelf life and quality of cut apple. International Journal of Life Sciences 9, 55-60.
Kondo N, Ahmad U, Monta M, Murasc H. 2000. Machine vision based quality evaluation of Iyokan orange fruit using neural networks. Computers and Electronics in Agriculture 29, 135-147.
Lelièvre J, Amor M, Flores B, Gomez M, El-Yahyaoui F, Chatenet C, Périn C, Hernandez J.O.S.E.A, Romojaro F, Latché A, Bouzayen M, Pitrat M, Dogimont C, Pech L. 2000. Ethylene-regulated genes and clarification of the role of ethylene in the regulation of ripening and quality. Acta Horticulturae 510, 499-509.
Li X, Lee W.S, Li M, Ehsani R, Mishra A.R, Yang C, Mangan R.L. 2012. Spectral difference analysis and airborne imaging classification for citrus greening infected trees. Computers and Electronics in Agriculture 83, 32-46.
Lidster P.D, Lawrence R.A, Blanpied G.D, Mcrae K.B. 1985. Laboratory evaluation of potassium permanganate for ethylene removal from CA apple storages. T. ASABE. 28:331-334.
Paliwal J, Visen N.S, Jayas D.S, White N.D.G. 2003. Comparison of a neural network and a non-parametric classifier for grain kernel identification. Biosystems Engineering 85, 405-413.
Park B, Lawrence K.C, Windham W.R, Chen Y.R, Chao K. 2002. Discriminate analysis of dual-wavelength spectral images for classifying poultry carcasses. Computers and Electronics in Agriculture 33, 219-231.
Pokharkar S.R, Thool V.R. 2012. Early pest identification in greenhouse crops using image processing techniques. International Journal of Computer Science and Network 1, 162-167.
Seglie L, Devecchi M, Trotta F, Scariot V. 2013. β-Cyclodextrin-based nanosponges improve 1-MCP efficacy in extending the postharvest quality of cut flowers. Scientia horticulturae 159, 162-165.
Seglie L, Martina K, Devecchi M, Roggero C, Trotta F, Scariot V. 2011. β-Cyclodextrin-based nanosponge as carrier for 1-MCP in extending the postharvest longevity carnation flowers, an evaluation of different degrees of cross-linking. Journal of Plant Growth Regulation 65, 505-511.
Serek M, Sisler E.C. 2005. Impact of 1-MCP on postharvest quality of ornamentals in APEC Symposium on Quality Management of Postharvest Systems, 2004, Bangkok, Thailand 121-128.
Serek M, Sisler E.C, Frello S, Sriskandarajah S. 2006. Postharvest technologies for extending the shelf life of ornamental crops. International Journal of Postharvest Technology and Innovation 1, 69-75.
Story D, Kacira M, Kubota C, Akoglu A, An L. 2010. Lettuce calcium deficiency detection with machine vision computed plant features in controlled environments. Computers and Electronics in Agriculture 74, 238-243.
Tatsuki M, Endo A, Ohkawa H. 2007. Influence of time from harvest to 1-MCP treatment on apple fruit quality and expression of genes for ethylene biosynthesis enzymes and ethylene receptors. Postharvest Biologyand Technology 43, 28-35.
Ushada D, Murase H, Fukuda H. 2007. Non-destructive sensing and its inverse model for canopy parameters using texture analysis and artificial neural network. Computers and Electronics in Agriculture 57, 149-165.
Van Doorn W.G. 2001. Categories of petal senescence and abscission: a re-evaluation. Annals of Botany 87, 447-456.
Zheng C, Sun D.W, Zheng L. 2006. Recent applications of image texture for evaluation of food qualities-a review. Trends in Food Science and Technology 17, 113-128.
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