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ارائه روشی مبتنی بر پردازش تصویر و شبکه عصبی مصنوعی برای استفاده در تنظیم خودکار سرزن پیاز | ||
| مهندسی بیوسیستم ایران | ||
| مقاله 8، دوره 51، شماره 2، تیر 1399، صفحه 319-328 اصل مقاله (920.49 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijbse.2020.286007.665205 | ||
| نویسندگان | ||
| مسلم افروز1؛ بابک بهشتی2؛ محسن حیدری سلطان آبادی* 3؛ محمد رضا ابراهیم زاده4 | ||
| 1دانشجوی دکتری، گروه مکانیک ماشینهای کشاورزی، دانشگاه آز اد اسلامی، واحد علوم و تحقیقات تهران، تهران، ایران | ||
| 2استادیار گروه مکانیک ماشینهای کشاورزی، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات تهران، تهران، ایران | ||
| 3استادیار پژوهشی /مرکز تحقیقات کشاورزی و منابع طبیعی اصفهان | ||
| 4استادیار گروه کشاورزی، دانشکده فنی و مهندسی، دانشگاه آزاد اسلامی، واحد یادگار امام، تهران، ایران | ||
| چکیده | ||
| سرزن پشت تراکتوری از جمله فناوری‌هایی است که برای حذف برگ پیاز از آن استفاده می‌شود. در این ماشین موقعیت قرار‌گیری‌های تیغه‌ها نقش به‌سزایی در کیفیت سرزنی پیازها دارد. در صورت برقراری ارتباط بین خصوصیات فیزیکی پیازها و طول برگ باقی‌مانده پس از سرزنی می‌توان به ارائه روش‌هایی برای تنظیم خودکار تیغه‌ها پرداخت. در این تحقیق روشی ارائه گردید که طبق آن قطر پیازها قبل از سرزنی به کمک پردازش تصویر محاسبه گردید. سپس طول برگ باقی‌مانده روی پیاز در جریان سرزنی با استفاده از شبکه عصبی پرسپترون چندلایه (MLP) تخمین زده شد و در ادامه با به‌کارگیری شبکه عصبی چندی‌ساز بردار یادگیر (LVQ) پیازها بر حسب اندازه طول برگ باقی‌مانده در چهارگروه طبقه‌بندی شدند. برای ارزیابی شبکه‌های مورد استفاده از آماره‌های ریشه میانگین مربعات خطا، میانگین خطای مطلق و دقت، صحت، حساسیت و اختصاصی بودن طبقه‌بندی استفاده شد. نتایج نشان داد که شبکه عصبی طراحی شده ارتفاع برش برگ را با مقادیر RMSE و MAE به ترتیب 025/0 و 01/0 پیش‌بینی نمود. همچنین طبقه‌بندی پیازها با دقت کلی 91 درصد انجام شد. نتایج این پژوهش را می‌توان در راه اندازی مکانیزم‌های خودکار برای تنظیم تیغه‌های برش سرزن پیاز به‌کار گرفت. | ||
| کلیدواژه‌ها | ||
| سرزن پیاز؛ پردازش تصویر؛ شبکه عصبی مصنوعی؛ چندی ساز بردار یادگیر؛ تنظیم خودکار | ||
| عنوان مقاله [English] | ||
| Provide a Method Based on Image Processing and Artificial Neural Network for Using on Automatic Adjustment of Onion Topper | ||
| نویسندگان [English] | ||
| Moslem Afruz1؛ Babak Beheshti2؛ Mohsen Heidarisoltanabadi3؛ MOHAMMAD REZA Ebrahimzadeh4 | ||
| 1Ph.D. Student, Department of Mechanic of Agricultural Machinery, Tehran Science and Research Branch, Islamic Azad University, Tehran, Iran | ||
| 2Assistant Professor, Department of Mechanic of Agricultural Machinery, Tehran Science and Research Branch, Islamic Azad University, Tehran, Iran | ||
| 3Member of scientific staff/Esfahan Center of Agricultural and Natural Resource Research | ||
| 4Assistant Professor, Department of Engineering, Agricultural Group, Yadegar -e- Imam Khomeini (RAH) Branch, Islamic Azad University, Tehran, Iran | ||
| چکیده [English] | ||
| Tractor mounted onion topper is one of the technologies used to remove onion leaves. The position of the blades in this machine plays an important role in the quality of the onion topping. In the case of communication between the physical characteristics of the bulbs and the length of the leaves remaining after the topping, it is possible to provide methods for automatic adjustment of the blades. In this research, a method was proposed to calculate the diameters of the bulbs before topping using image processing. Then the remaining leaf length on onions was estimated in topping process using the Multi-Layer perceptron (MLP) and the bulbs were classified in four groups according to the size of the leaves remaining by using the Learning Vector Quantization (LVQ). The statically parameters such as root mean square error, mean absolute error, specificity, precision, sensitivity and accuracy were used to evaluate the networks. The results showed that the designed neural network predicted leaf cutting height with RMSE and MAE values of 0.025 and 0.01 respectively. Also, the classification of onions was carried out with a total accuracy of 91%. The results of this research can be used to set up automated mechanisms of cutting blades in onion topper. | ||
| کلیدواژه‌ها [English] | ||
| Onion topper, Image processing, Neural network, Learning vector Quantization, Automatic adjustment | ||
| مراجع | ||
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