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استفاده از ماتریس هم-رخدادی سطح خاکستری برای طبقه بندی کشمش تودهای | ||
| مهندسی بیوسیستم ایران | ||
| مقاله 17، دوره 50، شماره 4، بهمن 1398، صفحه 951-961 اصل مقاله (1.22 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijbse.2019.274848.665153 | ||
| نویسندگان | ||
| مصطفی خجسته نژند* 1؛ حامد رمضانی2 | ||
| 1استادیار، گروه مهندسی مکانیک، دانشکده فنی و مهندسی، دانشگاه بناب، بناب، ایران | ||
| 2گروه 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 | ||
| کشمش؛ ماشین بردار پشتیبان؛ طبقه بندی؛ تحلیل تفکیک خطی؛ ماشین بینایی | ||
| عنوان مقاله [English] | ||
| Bulk Raisin Classification using Gray Level Co-occurrence Matrix | ||
| نویسندگان [English] | ||
| Mostafa Khojastehnazhand1؛ Hamed Ramezani2 | ||
| 1Assistant Professor, Mechanical Engineering Department, Faculty of Engineering, University of Bonab, Bonab, Iran | ||
| 2Department 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 | ||
| مراجع | ||
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