استفاده از ماتریس هم-رخدادی سطح خاکستری برای طبقه بندی کشمش توده‌ای‌

نوع مقاله: مقاله پژوهشی

نویسندگان

1 استادیار، گروه مهندسی مکانیک، دانشکده فنی و مهندسی، دانشگاه بناب، بناب، ایران

2 گروه مهندسی مکانیک بیوسیستم، دانشکده کشاورزی، دانشگاه تربیت مدرس، تهران، ایران

چکیده

کشمش یکی از محصولات مهم کشاورزی است. در این تحقیق با استفاده از روش بینایی اقدام به کیفیت سنجی محصول توده­ای کشمش در دو حالت متفاوت شده است. در حالت اول، 6 طبقه ترکیبی از کشمش خوب و بد و در حالت دوم 15 طبقه ترکیبی از کشمش خوب، بد و چوب و خار و خاشاک مورد بررسی قرار گرفته است. نتایج طبقه­بندی با روش­های LDA و SVM نشان دادند که بهترین دقت طبقه­بندی 6 طبقه، با روش SVM خطی حاصل شد که دارای دقت 55/85 درصد بوده است. نتایج حاصل برای طبقه­بندی 15 طبقه شامل کشمش خوب، بد و خار و خاشاک نشان داد که بهترین نتیجه باز با روش SVM خطی ولی با دقتی پایین‌تر در حدود 55/63 درصد حاصل گردید. نتایج نشان داد که روش GLCM بصورت قابل قبولی قادر به تشخیص طبقه محصول توده­ای کشمش بوده و می­تواند جایگزین فرد خبره در کارخانه­های فرآوری کشمش شود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Bulk Raisin Classification using Gray Level Co-occurrence Matrix

نویسندگان [English]

  • Mostafa Khojastehnazhand 1
  • Hamed Ramezani 2
1 Assistant Professor, Mechanical Engineering Department, Faculty of Engineering, University of Bonab, Bonab, Iran
2 Department of Biosystems Engineering, Faculty of Agricultural Engineering, Tarbiat Modares University, Tehran, Iran
چکیده [English]

Raisin is one of the most important agricultural products. In this study, by using the machine vision approach, the quality of bulk raisin was evaluated in two different conditions. In the first case, six classes of good and bad raisins mixture, and in the latter case, 15 classes of good, bad and woody raisins have been studied. Classification results with Linear Discriminate Analysis (LDA) and Support Vector Machine (SVM) showed that the best classification accuracy of 6 classes was obtained by linear SVM method with an accuracy of 85.55%. The results for classifying 15 classes including good, bad and wood showed that the best result was obtained by linear SVM method but with a lower accuracy of 63.55%. The results showed that the GLCM method was able to detect the class of raisin bulk product and could replace the expert in raisin processing plants.

کلیدواژه‌ها [English]

  • Raisin
  • Support vector machine
  • Classification
  • Linear Discrimination Analysis
  • Machine Vision

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