تخمین تازگی گوشت مرغ مبتنی بر تکنیک‌های پردازش تصویر و هوش مصنوعی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی مکانیک بیوسیستم، دانشگاه لرستان، خرم آباد

2 گروه مهندسی مکانیک بیوسیستم دانشگاه لرستان

3 دانشگاه لرستان

چکیده

در پژوهش حاضر روش­های نوین نظیر پردازش تصویر و هوش مصنوعی برای ارزیابی سریع، غیر مخرب و آنلاین تازگی گوشت مرغ بکار گرفته شده است. پس از تهیه تصاویر گوشت مرغ و عملیات پیش پردازش، تصاویر به کانال­های رنگی مختلف منتقل و ویژگی‌های آماری بافت تصاویر استخراج گردید. عملیات انتخاب ویژگی با ترکیب دو روش الگوریتم ازدحام ذرات و طبقه­بند شبکه‌های عصبی مصنوعی به منظور کاهش حجم محاسبات و ارتقای شاخص­های طبقه­بندی انجام شد. با توجه به تعداد ویژگی‌های منتخب، تعداد نرون­های موجود در لایه ورودی 22 عدد به دست آمد و تعداد نرون­های موجود در لایه خروجی براساس طبقه‌بندی تصاویر به صورت 5 کلاس؛ روز اول، روز دوم،...و روز پنجم، 5 عدد تعیین شد. در نهایت ساختار 5-8-22 به عنوان ساختار بهینه طبقه­بند مورد نظر حاصل شد. به منظور ارزیابی عملکرد طبقه­بند جهت تخمین تازگی گوشت مرغ، شاخص‌های آماری نظیر دقت، صحت، حساسیت، اختصاصی بودن و سطح زیر منحنی محاسبه شدند که مقادیر این شاخص­ها برای طبقه­بندی بر اساس ویژگی­های منتخب به ترتیب برابر 92، 02/80، 68/80، 89/94 و83/87 درصد می­باشند. نتایج حاصل از این مطالعه نشان می­دهد که سامانه پیشنهادی توانایی تشخیص میزان تازگی گوشت مرغ با دقت مناسب را دارد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Estimate freshness of chicken meat using image processing and artificial intelligent techniques

نویسندگان [English]

  • Sodabe Fatahi 1
  • Amin Taheri geravand 2
  • Feizollah Shahbazi 3
1 Mechanical Engineering of Biosystems Department, Lorestan University, Khorramabad, Iran
2 university of Lorestan
3 Lorestan University
چکیده [English]

In the current study, new methods such as image processing and artificial intelligence have been used for the fast, easy and non-destructive evaluation of chicken meat freshness. After image acquisitions and pre-processing operations, the images were transferred to different color spaces and the statistical texture features of images were extracted. The feature selection operation was performed by combining particle swarm optimization (PSO) and artificial neural networks (ANNs) classifier to reduce the amount of calculations and improve the classification indicators. According to the number of selected features, the number of existing neurons in input layer were obtained 22 and the number of existing neurons in output layer were determined 5, according to classify the images as 5 classes. In the purpose of the classifier assessment operation to estimate the freshness of chicken meat, the statistical indicators such as precision, accuracy, sensitivity, specificity and area under the curve were calculated, which the values of these indicators for classification based on the selected features are 92, 80.02, 80.68, 94.89 and 87.83 percent, respectively. The obtained results of this study indicates that suggested system has the ability to diagnosis the chicken meat freshness with suitable accuracy.

کلیدواژه‌ها [English]

  • Chicken meat
  • Freshness diagnosis
  • image processing
  • Artificial neural networks (ANNs)
  • Particle Swarm Optimization (PSO)
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