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
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