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Computer Vision Based Date Fruit Grading System: Design And Implementation

المصدر: مجلة جامعة الملك سعود - علوم الحاسب والمعلومات
الناشر: جامعة الملك سعود
المؤلف الرئيسي: Al Ohali, Yousef Nasser (Author)
المجلد/العدد: مج23, ع1
محكمة: نعم
الدولة: السعودية
التاريخ الميلادي: 2011
الصفحات: 29 - 36
DOI: 10.33948/0584-023-001-004
ISSN: 1319-1578
رقم MD: 971946
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: science
مواضيع:
كلمات المؤلف المفتاحية:
Fruit Classification | Pattern Recognition | Image Processing | Neural Networks | Automation In Agriculture
رابط المحتوى:
صورة الغلاف QR قانون
حفظ في:
LEADER 02358nam a22002297a 4500
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024 |3 10.33948/0584-023-001-004 
041 |a eng 
044 |b السعودية 
100 |9 16382  |a Al Ohali, Yousef Nasser  |e Author 
245 |a Computer Vision Based Date Fruit Grading System: Design And Implementation 
260 |b جامعة الملك سعود  |c 2011 
300 |a 29 - 36 
336 |a بحوث ومقالات  |b Article 
520 |b The Kingdom of Saudi Arabia is the world's largest producer of date fruit. It produces almost 400 date varieties in bulk. During the harvesting season the date grading and sorting pose problems for date growers. Since it is a labor intensive and time consuming process, it delays the post harvesting operations which costs them dearly. The date grading and sorting is a repetitive process. In practice, it is carried out by humans manually through visual inspection. The manual inspection poses further problems in maintaining consistency in grading and uniformity in sorting. To speed up the process as well as maintain the consistency and uniformity we have designed and implemented a prototypical computer vision based date grading and sorting system. We have defined a set of external quality features. The system uses RGB images of the date fruits. From these images, it automatically extracts the aforementioned external date quality features. Based on the extracted features it classifies dates into three quality categories (grades 1, 2 and 3) defined by experts. We have studied the performance of a back propagation neural network classifier and tested the accuracy of the system on preselected date samples. The test results show that the system can sort 80% dates accurately. 
653 |a برامج الحاسوب  |a الشبكات العصبية  |a تصميم البرامج  |a فرز التمور 
692 |b Fruit Classification  |b Pattern Recognition  |b Image Processing  |b Neural Networks  |b Automation In Agriculture 
773 |c 004  |e Journal of King Saud University (Computer and Information Sciences)  |f Maǧalaẗ ǧamʼaẗ al-malīk Saud : ùlm al-ḥasib wa al-maʼlumat  |l 001  |m مج23, ع1  |o 0584  |s مجلة جامعة الملك سعود - علوم الحاسب والمعلومات  |v 023  |x 1319-1578 
856 |u 0584-023-001-004.pdf 
930 |d y  |p y 
995 |a science 
999 |c 971946  |d 971946 

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