طبقه‌بندی محصول رسیده و نارس میوه توت سفید به کمک پردازش تصاویر حرارتی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد، گروه مهندسی ماشین های کشاورزی، بردیس کشاورزی و منابع طبیعی، دانشگاه تهران،کرج،ایران

2 استادیار گروه مهندسی مکانیک ماشین های کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران ، کرج ،ایران

چکیده

میوه توت به دلیل خواص بی شمار پزشکی و تغذیه­ای، به عنوان میوه­ای محبوب شناخته می­شود. این پژوهش به منظور جداسازی توت­های رسیده از نارس در نظر دارد، روشی را  به کمک تصاویر حرارتی برپایه ترموگرافی فعال معرفی نماید. آزمایشات با 70 نمونه میوه توت که به صورت تصادفی از یک درخت انتخاب شده بودند و توسط پنج فرد متخصص از روی رنگ و بافت، به دو طبقه رسیده و نارس تقسیم شده بودند، انجام گردید. تغییرات دمای نمونه‌ها با وارد کردن شوک حرارتی توسط دوربین حرارتی ثبت گردید و ضرایب مدل های درجه اول، درجه دوم و لگاریتمی برازش داده شده با نمودارهای دما-زمان برای طبقه بندی در نرم افزار متلب به کار گرفته شد. دقت طبقه­بندی توت­ها به کمک این نرم افزار و PCA[1]، که تنها محدود به استفاده از ضرایب معادله درجه اول شده بود، مقدار90 درصد به دست آمد. با استفاده از نتایج این پژوهش می­توان سرعت و دقت طبقه­بندی را در خطوط طبقه­بندی میوه توت به طور مؤثری بالا برد.



1- Principal component analysis

کلیدواژه‌ها


عنوان مقاله [English]

Classification of Ripe and Unripe White Berry Fruit Using Thermal Image Processing

نویسندگان [English]

  • parsa haidary 1
  • Ali Hajiahmad 2
  • Hossien roshan ghyasi 1
1 M.Sc. student, Department of agricultural machinery engineering, college of agriculture and natural resources, Tehran university, Karaj, Iran
2 Assistant professor, Department of agricultural machinery engineering, college of agriculture and natural resource, Tehran university,Karaj, Iran
چکیده [English]

Since white berry has much medical and edible benefit, it is known as a popular fruit. This research is carried out in order to classify ripe and unripe berries from each other using active thermography method. In this research 70 berries have been used as experimental samples randomly selected from a tree. They were sorted to “ripe” and “unripe” class by 5 expert people with attention to the color and texture of fruits. The temperature changes of samples by heat shock induction were recorded with a thermal camera and coefficients of first order, second order and logarithmic equations fitted to temperature-time graphs were employed for classification in MATLAB software. Using Principal Component Analysis (PCA) method, in MATLAB software, causes to use only first order equation coefficients, with an accuracy of 90 percent. Using the results of this research, the speed and accuracy of classification can be effectively increased in berry classification lines.

کلیدواژه‌ها [English]

  • Natural network
  • Classification
  • PCA
  • thermography
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