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شناسایی ژنوتیپ های سیب زمینی پس از دورۀ انبارمانی با استفاده از ترکیب روش های پردازش تصویر و شبکۀ عصبی مصنوعی | ||
| تحقیقات سامانه‌ها و مکانیزاسیون کشاورزی | ||
| مقاله 10، دوره 21، شماره 74، شهریور 1399، صفحه 135-150 اصل مقاله (1.31 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/erams.2019.124819.1295 | ||
| نویسندگان | ||
| رویا فرهادی1؛ امیرحسین افکاری سیاح* 2؛ بهاره جمشیدی3؛ احمد موسی پور گرجی4 | ||
| 1دانشجوی دکتری رشته مهندسی مکانیک بیوسیستم، گروه مهندسی بیوسیستم، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی. اردبیل. ایران | ||
| 2دانشیار گروه مهندسی بیوسیستم، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران | ||
| 3دانشیار مؤسسه تحقیقات فنی و مهندسی کشاورزی، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرج، ایران. | ||
| 4استادیار موسسه تحقیقات اصلاح و تهیه نهال و بذر، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرج، ایران | ||
| چکیده | ||
| برای ایجاد اطمینان از عرضۀ پیوستۀ سیب زمینی به مصرفکنندگان و صنایع فرآوری این محصول، ذخیره سازی آن ضروری است. هنگام ذخیره سازی، تغییرات فیزیولوژیکی و اتلاف آب در سیب زمینی باعث می شود رنگ، شکل، اندازه و بافت آن تغییرکند. بنابراین، برای تعیین کیفیت محصول نیاز به روش های دقیق و سریع خواهد بود. در این مطالعه، از روش ماشین بینایی و شبکۀ عصبی مصنوعی برای کلاسه بندی و مدلسازی دو نمونه سیب زمینی ذخیره شده در شرایط ثابت و متغیر استفاده شد. از بین 29 پارامتر اندازه گیری شده مربوط به ویژگی های رنگ، بافت و مورفولوژیکی، سه ویژگی طول قطر بزرگ، فشردگی و مساحت (از ویژگی های مورفولوژیکی)، دو ویژگی L* و b* (از ویژگی های رنگی) و 2 ویژگی میانگین کنتراست و میانگین سطح خاکستری (از ویژگی های بافت) به عنوان پارامترهای اصلی انتخاب شدند. در میان الگوریتم های آموزشی، الگوریتم آموزشی لونبرگ-مارکوارت (LM) با کمترین میانگین مربعات خطا 0/012=RMSE و بیشترین ضریب تبیین 95/01 =R2 درصد مدلی بهینه در طبقه بندی (کلاسه بندی) دو نمونه سیب زمینی انبار شده در انبار غیرفنی و فنی شناخته شد. در انبار غیرفنی و فنی، دقت شناسایی ژنوتیپ اگریا به ترتیب 89/2 و 87/6 درصد و دقت شناسایی ژنوتیپ کلون 8-397009 بهترتیب 92/4 و 90/3 درصد به دست آمد. | ||
| کلیدواژه‌ها | ||
| تغییرات فیزیولوژیکی؛ طبقه بندی؛ کیفیت؛ ماشین بینایی | ||
| عنوان مقاله [English] | ||
| Identify of Potato Genotypes by Combining Machine Vision Techniques and Neural Networks after the Storage Period | ||
| نویسندگان [English] | ||
| roya farhadi1؛ Amir Hossein Afkari Sayyah2؛ Bahareh Jamshidi3؛ ahmad mousapour gorji4 | ||
| 1Ph.D Student in Mechanical Biosystem Engineering, Department of Biosystems Engineering, Faculty of Agricultural and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran. | ||
| 2Associate Professor, Department of Biosystem Engineering, Faculty of Agricultural and Natural Resources, University of Mohaghegh-Ardabili, Ardabil, Iran | ||
| 3Associate Professor, Agricultural Engineering Research Institute, Agricultural Research Education and Extension Organization, Karaj, Iran | ||
| 4Assistant Professor, Seed and Plant Improvement Research Institute, Agricultural Research, Education and Extension Organization, Karaj, Iran | ||
| چکیده [English] | ||
| Potato storage is essential to ensure the continued supplying of potatoes to consumers and the potato processing industry. During storage, physiological changes and water loss lead to changes in color, shape, size, and texture of potatoes. Therefore, there is a need for a quick and accurate method to measure the quality of the product. In this study, machine vision and neural network methods were used in classification and modeling of two stored potato samples (Agria and Clone 8-397009) under constant and variable conditions. Among 29 measured parameters relating to color, texture and morphological features of potato, some features were selected as the main parameters to monitor the chnges in product during storage period: Major Axis Length, Compactness, and area (morphological features), L* and b* (color features) and Average contrast (Ac) and Average gray level (Agl) (texture features). Among the training algorithms, Levenberg–Marquardt (LM) training algorithm with the lowest root mean square error (RMSE=0.012) and the highest coefficient of determination (R2=95.01) were considered as an optimal model for classification of two samples stored in non-technical and technical storage. The accuracy of identification of the Agria genotype was 89.2% and 87.6%, and the accuracy of the genotype Clone 8-397009 was 92.4% and 90.3%, in non-technical and technical storage respectively. | ||
| کلیدواژه‌ها [English] | ||
| Classification, Machine Vision, Physiological Changes, Quality | ||
| مراجع | ||
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