سامانه برآورد وزن‌ جوجه‌های گوشتی به صورت جداگانه با استفاده از پردازش تصویر و آنالیز رگرسیون چندگانه

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد دانشکده مهندسی زراعی و عمران روستایی، گروه مکانیک بیوسیستم، دانشگاه کشاورزی و منابع طبیعی رامین خوزستان

2 دانشگاه رامین خوزستان

3 استادیار دانشکده علوم دامی و صنایع غذایی، گروه علوم دامی، دانشگاه کشاورزی و منابع طبیعی رامین خوزستان

چکیده

هدف از این پژوهش بررسی امکان تشخیص تغییرات روزانه وزن جوجه­های گوشتی با استفاده از پردازش تصویر و آنالیز رگرسیون چندگانه است. بدین منظور تعداد30 قطعه جوجه گوشتی یک روزه تحت شرایط استاندارد پرورش داده شد. روزانه بعد از اخذ تصاویر، جوجه­ها به صورت جداگانه وزن می­شدند. از 2490 تصویر اخذشده، شش ویژگی (مساحت، محیط، مساحت محدب، قطر بزرگ، قطر کوچک، خروج ازمرکز) استخراج و به منظور توسعه مدل­های رگرسیونی مورد استفاده قرار گرفتند. روابط خطی بین وزن بدن و این شش ویژگی استخراج شده از تصویر به صورت جداگانه نشان دادند که مقادیر بدست آمده برای این ویژگی­ها به جز خروج از مرکز برای هر پرنده به صورت جداگانه بالای 9/0 هستند. در ضمن به منظور توسعه مدل رگرسیونی چندگانه و حذف پارامترهای ورودی به این رابطه از روش گام­به­گام استفاده گردید.  بر اساس مدل توسعه یافتهمساحت، محیط، مساحت محدب، قطر بزرگ، قطر کوچک، اثر متقابل مساحت و قطر بزرگ، مساحت محدب و محیط، قطر بزرگ و قطر کوچک توانایی پیشگویی وزن را با 945/0 =، مقادیرخطای رگرسیون استاندارد (g)934/88­ و مقدار دقت نسبی (%)12/0 در سطح احتمال 5% دارا می­باشد. این مساله حاکی از توانایی پردازش تصویر و رگرسیونی چندگانه در پیشگویی وزن دارد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Weight estimation system of individual broiler chickens using digital image processing and multi-regression analysis

چکیده [English]

The purpose of this study was to identify daily changes in body weight of broiler chickens using image processing and multi-regression analysis. Therefore, thirty 1-day-old broiler chickens were reared under standard rearing condition and after acquiring images they were weighted, individually. From 2490 digital images, six features (perimeter, area convex, major axis, minor axis, eccentricity) were extracted. Linear equations between body weight and these features indicated that R2 values for these features (except for eccentricity) for the individual birds were higher than 0.9. Furthermore, stepwise selection process was utilized to develop multi-regression model and to remove non-significant factors from the regression equation. According to the developed equation, area, perimeter, area convex, major axis, minor axis, interaction between area and major axis, and convex area and perimeter, major and minor axis were capable of predicting weight with R2= 0.945 in the confidence level of %5. This shows that the digital image processing and multi-regression analysis could predict weight of life chickens, promisingly.

کلیدواژه‌ها [English]

  • Weight prediction
  • Digital image analysis
  • Multi-regression analysis
  • Broiler Chicken
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