کاربردسیستم بینایی ماشین درطبقه‌بندی ازگیل طی دوران رسیدگی در سردخانه

نوع مقاله: مقاله پژوهشی

نویسندگان

1 استادیار گروه علوم و صنایع غذایی، دانشکده کشاورزی، دانشگاه زنجان

2 دانشیار، گروه علوم و مهندسی صنایع، دانشگاه زنجان، زنجان، ایران

چکیده

درجه‌بندی میوه‌ها بر اساس درجه رسیدگی، عمر انبارمانی یا کیفیت ظاهری از اهمیت بالایی برای صنایع، سردخانه‌ها و حتی باغات میوه برخوردار است. پیشرفت‌های اخیر در زمینه سیستم ماشین بینایی فرصت‌های مناسبی برای کاربرد آن در زمینه علوم کشاورزی و صنایع غذایی فراهم آورده است. ازگیل Mespilus germanica)) به سبب تغییرات فیزیکی، شیمیایی طی نگهداری شدیداً دچار افت کیفیت می‌گردد. لذا هدف از انجام پژوهش حاضر ایجاد یک سیستم درجه‌بندی ازگیل بر اساس خصیصه‌های رنگی می‌باشد. به این منظور ابتدا خصوصیات فیزیکی و شیمیایی اندازه‌گیری شد و ازگیل‌ها با کمک شاخص رسیدگی به سه گروه گروه ازگیل تازه (FM)، ازگیل کامل رسیده (RM) و ازگیل بیش از حد رسیده (ORM) طبقه‌بندی شدند. فرآیند طبقه‌ بندی با استفاده از تجزیه و تحلیل مولفه‌های اصلی (PCA) و تجزیه و تحلیل چند متغیره (MDA) با هدف تنها استفاده از پارامترهای رنگی برای برآورد میزان رسیدگی و زمان نگهداری اعمال شد. بر اساس مقدار ضریب همبستگی (بالاتر از 9/0 در بسیاری از پارامترها)، وجود همبستگی بالا بین پارامترهای فیزیکی و شیمیایی با خصوصیات رنگی تایید شد. اولین و دومین تجزیه و تحلیل مولفه اصلی با و بدون حضور پارامترهای فیزیکی، شیمیایی به ترتیب با قابلیت اطمینان 11/92 و 31/95 درصد به دست آمد. تجزیه و تحلیل چند متغیره نرخ طبقه‌بندی 08/96 درصد را تنها با استفاده از پارامترهای رنگی فراهم آورد. نتایج بدست آمده نشان داد که استفاده از سیستم ماشین بینایی به عنوان یک روش غیر مخرب در ارزیابی میزان رسیدگی ازگیل بر اساس خصیصه‌های رنگی مناسب، کارآمد، سریع و موثر می‌باشد.

چکیده تصویری

کاربردسیستم بینایی ماشین درطبقه‌بندی ازگیل طی دوران رسیدگی در سردخانه

تازه های تحقیق

  • وجود همبستگی بالا بین پارامترهای فیزیکی و شیمیایی با خصوصیات رنگی ازگیل تایید شد.
  • امکان استفاده از سیستم ماشین بینایی به عنوان یک روش غیر مخرب در ارزیابی میزان رسیدگی ازگیل
  • تجزیه و تحلیل چند متغیره نرخ طبقه‌بندی 08/96 درصد را تنها با استفاده از پارامترهای رنگی فراهم آورد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Computer Vision System Applied to Classification of Medlar (Mespilus germanica) during ripening stage at cold storage

نویسندگان [English]

  • Mohsen Zandi 1
  • Ali Ganjloo 2
  • Mandana Bimakr 2
1 Department Food Science and Technology, Faculty of Agriculture, Zanjan University, Zanjan, Iran, P.O.Box 45195-313, IRAN
2 Associate Professor, Department of Food Science and Engineering, University of Zanjan, Zanjan, Iran
چکیده [English]

Today, computer vision system (CVS) has become an alternative to visual inspection being objective, consistent, rapid, and economical in various agricultural and food industry commodity grading systems,. A particular application of this technology is the estimation of ripening or the study of the evolution of maturity of several produce in order to improve storage conditions or to be able to offer consumers better products. Short storage life of medlar fruit (Mespilus germanica) and its high susceptibility to water loss and browning are the main factors limiting its marketability. The aim of this work was to implement a straightforward and low-cost method at laboratory scale as an initial approach, in order to determine the ripening stages of M. germanica by means of a CVS and multivariate analysis. In the present work, physicochemical properties and color parameters obtained using a CVS at laboratory level were linked to establish the ripening stages of M. germanica. To classify the stages, a ripening index (RPI) was proposed, in which three stages were identified; unripe, ripe and senescent. Two classifiers based on principle component analysis (PCA) and multivariate discriminant analysis (MDA) were used to assess the applicability of vision system. The color parameters correlate correctly with the physicochemical changes which are considered the standard method to evaluate the maturity of fruits. PCA made it possible to obtain classification rates of 92.11% and 95.31% with and without physicochemical parameters, respectively. MDA was capable of classifying apples in their correct ripening stage with 96.08% accuracy. The results obtained showed that CVS developed for the study can be used as a useful non-invasive, efficient method for the evaluation of the ripeness of mangoes.

کلیدواژه‌ها [English]

  • Image Processing
  • Medlar
  • Multivariate analysis
  • Visual properties
  • Quality classification
[1] Afsharnia, F., Mehdizadeh, S. A., Ghaseminejad, M., & Heidari, M. The effect of dynamic loading on abrasion of mulberry fruit using digital image analysis. Information Processing in Agriculture, 4(4), 291-299. (2017)
[2] Al-Amoudi, R. H., Taylan, O., Kutlu, G., et al. Characterization of chemical, molecular, thermal and rheological properties of medlar pectin extracted at optimum conditions as determined by Box-Behnken and ANFIS models. Food Chemistry, 271, 650-662. (2019)
[3] Arakeri, M. P. Computer vision based fruit grading system for quality evaluation of tomato in agriculture industry. Procedia Computer Science, 79, 426-433. (2016)
[4] Arzate-Vázquez, I., Chanona-Pérez, J. J., de Jesús Perea-Flores, M., et al. Image processing applied to classification of avocado variety Hass (Persea americana Mill) during the ripening process. Food and Bioprocess Technology, 4(7), 1307-1313. (2011)
[5] Ashournezhad, M., & Ghasemnezhad, M. Effects of cellophane-film packaging and cold storage on the keeping quality and storage life of loquat fruit (Eriobotrya japonica). Iranian Journal of Nutrition Sciences & Food Technology, 7(2), 95-102. (2012)
[6] Baigvand, M., Banakar, A., Minaei, S., Khodaei, J., & Behroozi-Khazaei, N. Machine vision system for grading of dried figs. Computers and Electronics in Agriculture, 119, 158-165. (2015)
[7] Bhargava, A., & Bansal, A. Fruits and vegetables quality evaluation using computer vision: A review. Journal of King Saud University-Computer and Information Sciences. (2018)
[8] Cárdenas-Pérez, S., Chanona-Pérez, J., Méndez-Méndez, J. V., et al. Evaluation of the ripening stages of apple (Golden Delicious) by means of computer vision system. Biosystems Engineering, 159, 46-58. (2017)
[9] Gruz, J., Ayaz, F. A., Torun, H., & Strnad, M. Phenolic acid content and radical scavenging activity of extracts from medlar (Mespilus germanica L.) fruit at different stages of ripening. Food Chemistry, 124(1), 271-277. (2011)
[10] Hacıseferogˇulları, H., Özcan, M., Sonmete, M. H., & Özbek, O. Some physical and chemical parameters of wild medlar (Mespilus germanica L.) fruit grown in Turkey. Journal of Food Engineering, 69(1), 1-7. (2005)
[11] Helrich, K. (1990). Official methods of analysis of the AOAC International: Association of Official Analytical Chemists.
[12] Hosseinpour, S., Rafiee, S., Mohtasebi, S. S., & Aghbashlo, M. Application of computer vision technique for on-line monitoring of shrimp color changes during drying. Journal of Food Engineering, 115(1), 99-114. (2013)
[13] Huang, Y., Lu, R., & Chen, K. Development of a multichannel hyperspectral imaging probe for property and quality assessment of horticultural products. Postharvest Biology and Technology, 133, 88-97. (2017)
[14] Isbilir, S. S., Kabala, S. I., & Yagar, H. Assessment of in vitro Antioxidant and Antidiabetic Capacities of Medlar (Mespilus germanica).  Notulae Botanicae Horti Agrobotanici Cluj-Napoca, 47(2), 384-389. (2019)
[15] Jackman, P., & Sun, D.-W. Recent advances in image processing using image texture features for food quality assessment (Mespilus germanica) . Trends in Food Science & Technology, 29(1), 35-43. (2013)
[16] Khadivi, A., Rezaei, M., Heidari, P., Safari-Khuzani, A., & Sahebi, M. Morphological and fruit characterizations of common medlar (Mespilus germanica L.) germplasm. Scientia Horticulturae, 252, 38-47. (2019)
[17] Kienzle, S., Sruamsiri, P., Carle, R., Sirisakulwat, S., Spreer, W., & Neidhart, S. Harvest maturity specification for mango fruit (Mangifera indica L. ‘Chok Anan’) in regard to long supply chains. Postharvest Biology and Technology, 61(1), 41-55. (2011)
[18] Liming, X., & Yanchao, Z. Automated strawberry grading system based on image processing. Computers and Electronics in Agriculture, 71, S32-S39. (2010)
[19] Makky, M., & Soni, P. Development of an automatic grading machine for oil palm fresh fruits bunches (FFBs) based on machine vision. Computers and Electronics in Agriculture, 93, 129-139. (2013)
[20] Mamashloo, S., Sadeghi, M. A., Ghorbani, M., Alami, M., & Khomeiri, M. The evaluation of antioxidant properties and stability of phenolic copmpounds from medlar (Mespilus germanica L.)  fruit. (2012)
[21] Moallem, P., Serajoddin, A., & Pourghassem, H. Computer vision-based apple grading for golden delicious apples based on surface features. Information Processing in Agriculture, 4(1), 33-40. (2017)
[22] Mohammadi, V., Kheiralipour, K., & Ghasemi-Varnamkhasti, M. Detecting maturity of persimmon fruit based on image processing technique. Scientia Horticulturae, 184, 123-128. (2015)
[23] Momin, M., Rahman, M., Sultana, M., Igathinathane, C., Ziauddin, A., & Grift, T. E. Geometry-based mass grading of mango fruits using image processing. Information Processing in Agriculture, 4(2), 150-160. (2017)
[24] Muhammad, G. Date fruits classification using texture descriptors and shape-size features. Engineering Applications of Artificial Intelligence, 37, 361-367. (2015)
[25] Nordey, T., Léchaudel, M., Génard, M., & Joas, J. Spatial and temporal variations in mango colour, acidity, and sweetness in relation to temperature and ethylene gradients within the fruit. Journal of Plant Physiology, 171(17), 1555-1563. (2014)
[26] Nouri-Ahmadabadi, H., Omid, M., Mohtasebi, S. S., & Firouz, M. S. Design, development and evaluation of an online grading system for peeled pistachios equipped with machine vision technology and support vector machine. Information Processing in Agriculture, 4(4), 333-341. (2017)
[27] Nunes, M. C. N., Emond, J.-P., & Brecht, J. K. Quality curves for highbush blueberries as a function of the storage temperature. Small Fruits Review, 3(3-4), 423-440. (2004)
[28] Pourdarbani, R., Ghassemzadeh, H. R., Seyedarabi, H., Nahandi, F. Z., & Vahed, M. M. Study on an automatic sorting system for Date fruits. Journal of the Saudi Society of Agricultural Sciences, 14(1), 83-90. (2015)
[29] Raftani Amiri, Z., & Akbari, N. Evaluation of physicochemical and microbiological properties, antioxidant activities and phenolic Compounds of medlar  (Mespilus germanica L.) syrup [In persiam]. Food Science and Technology, 15(75), 81-89. (2018)
[30] Sabzi, S., Abbaspour-Gilandeh, Y., & García-Mateos, G. A new approach for visual identification of orange varieties using neural networks and metaheuristic algorithms. Information Processing in Agriculture, 5(1), 162-172. (2018)
[31] Salehi, F., & Kashsninejad, M. Physicochemical and Rheological Properties of Wild Medlar Concentrate [In persian]. Iranian Journal  Food Science And Technology, 13, 49-57. (2017)
[32] Sofu, M. M., Er, O., Kayacan, M., & Cetişli, B. Design of an automatic apple sorting system using machine vision. Computers and Electronics in Agriculture, 127, 395-405. (2016)
[33] Vélez-Rivera, N., Blasco, J., Chanona-Pérez, J., et al. Computer vision system applied to classification of “Manila” mangoes during ripening process. Food and Bioprocess Technology, 7(4), 1183-1194. (2014)
[34] Wan, P., Toudeshki, A., Tan, H., & Ehsani, R. A methodology for fresh tomato maturity detection using computer vision. Computers and Electronics in Agriculture, 146, 43-50. (2018)
[35] Zandi, M., Ganjloo, A., & Bimakr, M. Modelling medlar (Mespilus germanica) quality changes during cold storage using kinetics models and artificial neural network [In persian]. Journal of Food Science & Technology, 16(96), 103-119. (2020)