طراحی و ساخت دستگاه برش دقیق ساقه سبزی‌های خوراکی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانش آموخته کارشناسی ارشد، گروه مکانیک بیوسیستم، دانشگاه تربیت مدرس، تهران، ایران

2 عضو هیئت علمی گروه مهندسی مکانیک بیوسیستم دانشگاه تربیت مدرس

3 گروه مهندسی مکانیک بیوسیستم، دانشگاه تربیت مدرس، تهران، ایران

4 مدیر گروه مکانیک بیوسیستم دانشگاه تربیت مدرس.

چکیده

در فرآوری سبزی‌ها و همچنین آماده کردن برای مصرف تازه خوری، قسمت برگی سبزی از ساقه به‌صورت دستی و غیرمکانیزه جدا می‌گردد. این تحقیق به‌منظور طراحی و ساخت یک دستگاه خودکار برای برش دقیق ساقه سبزی‌های برداشت ‌شده از مزرعه انجام گرفت. برای تشخیص محل برش مناسب از بینایی ماشینی استفاده شد. بدین‌صورت که پس از قرار دادن محصول بر روی نوار نقاله، به‌صورت خودکار تصویربرداری می‌شود و پس از تشخیص محل برش، یک مکانیزم برشی قسمت برگی گیاه را از ساقه آن جدا می‌کند. سامانه برش طراحی شده برش نمونه‌ها را با دقتی حدود 3 میلی‌متر انجام داد. برای تشخیص محل برش و انجام آن حدود 4 ثانیه زمان صرف می‌شود، درنتیجه بیشینه ظرفیت دستگاه برابر با 15 برش در هر دقیقه است. محصول خروجی دستگاه در مواردی که سبزی‌های ورودی به‌صورت یکدست و تازه برداشت شده بودند، کیفیت مناسبی داشت.

کلیدواژه‌ها


عنوان مقاله [English]

Design and Development of an Automatic Precision Cutting System for Herb Stems

نویسندگان [English]

  • Mohammad Hosseinpour-Zarnaq 1
  • saeid minaei 2
  • Alireza Mahdavian 3
  • Mohammad Hadi Khoshtaghaza 4
1 Biosystems Engineering Department, Tarbiat Modares University, Tehran, Iran
2 Professor of biosystems engineering Tarbiat Modares Univ.
3 Biosystems Engineering Department, Tarbiat Modares University, Tehran, Iran
4 Biosystems Engineering Dept., Agricultural Faculty, Tarbiat Modares University
چکیده [English]

In the processing of vegetables as well as preparation for fresh consumption, the leaf part of the vegetable is separated from the stem manually and non-mechanically. This research was carried out in order to design and development an automatic system for precise cutting of vegetable stems harvested from the field. Machine vision was used to determine the appropriate cutting position. In this way, after placing the product on the conveyor, it is automatically photographed and after identifying the cutting location, a cutting mechanism separates the leaf part of the plant from its stem. The designed cutting system performed the cutting of the samples with an accuracy of about 3 mm. It takes about 4 seconds to identify the cutting location and perform cutting, so the maximum capacity of the machine is 15 cuts per minute. The output of the machine had acceptable quality in cases where the incoming vegetables were evenly and freshly harvested.

کلیدواژه‌ها [English]

  • precise cutting
  • vegetable
  • machine vision
Bhargava, A., & Bansal, A. (2018). Fruits and vegetables quality evaluation using computer vision: A review. Journal of King Saud University-Computer and Information Sciences.
Blasco, J., Aleixos, N., Roger, J. M., Rabatel, G., & Moltó, E. (2002). AE—Automation and emerging technologies: Robotic weed control using machine vision. Biosystems Engineering, 83(2), 149-157.
Brosnan, T., & Sun, D. W. (2004). Improving quality inspection of food products by computer vision––a review. Journal of food engineering, 61(1), 3-16.
Budynas, R. G., & Nisbett, J. K. (2008). Shigley's mechanical engineering design (Vol. 8). New York: McGraw-Hill.
Burgos-Artizzu, X. P., Ribeiro, A., Guijarro, M., & Pajares, G. (2011). Real-time image processing for crop/weed discrimination in maize fields. Computers and Electronics in Agriculture, 75(2), 337-346.
Di Leo, G., Liguori, C., Pietrosanto, A., & Sommella, P. (2017). A vision system for the online quality monitoring of industrial manufacturing. Optics and Lasers in Engineering, 89, 162-168.
Ghahrae, O., Khoshtaghaza, M. H., & Bin Ahmad, D. E. S. A. (2008). Design and development of special cutting system for sweet sorghum harvester. Journal of Central European Agriculture, 9(3), 469-474.
Gonzalez, R. C., Woods, R. E., & Eddins, S. L. (2009). Digital Image Processing using MATLAB; 2nd ed. Gatesmark Publishing: New Jersey, USA.
Hernández-Hernández, J. L., García-Mateos, G., González-Esquiva, J. M., Escarabajal-Henarejos, D., Ruiz-Canales, A., & Molina-Martínez, J. M. (2016). Optimal color space selection method for plant/soil segmentation in agriculture. Computers and Electronics in Agriculture, 122, 124-132.
Jafari, K. (1996). Design, Manufacturing the Measurement Unit of Plants Cutting Force and Designing the Cutting System of Atriplex Harvesting. [Unpublished Master’s thesis]. Faculty of Agriculture. Tarbiat Modares University.
Koocheki, A., Nassiri Mahallati, M., Hassanzadeh Aval, F., Mansoori, H., Amiri, S. R., Zarghani, H., & Karimian, M. (2013).  Agrobiodiversity of Vegetable Crops in Agroecosystems in Iran. Iranian Journal of Applied Ecology (ijae), 2(4), 1-12.
Lowe, A., Harrison, N., & French, A. P. (2017). Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress. Plant methods, 13(1), 80.
Medici, P. (2011). Pin-Hole Camera Reference Frame and Calibration Techniques.
Meng, Q., Qiu, R., He, J., Zhang, M., Ma, X., & Liu, G. (2015). Development of agricultural implement system based on machine vision and fuzzy control. Computers and Electronics in Agriculture, 112, 128-138.
Mollazade, K., Omid, M., & Arefi, A. (2012). Comparing data mining classifiers for grading raisins based on visual features. Computers and Electronics in Agriculture, 84, 124-131.
Mott, R. L., Vavrek, E. M., & Wang, J. (2018). Machine elements in mechanical design. Prentice Hall.
Nadafzadeh, M., & Abdanan Mehdizadeh, S. (2017). Determination of the most suitable color space for intelligent water stress discrimination for plants inside the greenhouse (Case Study: Coleus). Iranian Journal of Biosystems Engineering, 48(4), 407-418.
Olowojola, C. O., Faleye, T. &Agbetoye, L. A. S. (2011). Development and performance evaluation of a leafy vegetable harvester. International Research Journal of Agricultural Science and Soil Science, 1(7), 227-233.
Schuldt, S., Arnold, G., Kowalewski, J., Schneider, Y., & Rohm, H. (2016). Analysis of the sharpness of blades for food cutting. Journal of Food Engineering, 188, 13-20.
Sofu, M. M., Er, O., Kayacan, M. C., & Cetişli, B. (2016). Design of an automatic apple sorting system using machine vision. Computers and Electronics in Agriculture, 127, 395-405.
Sonawane, S. P., Sharma, G. P., & Pandya, A. C. (2011). Design and development of power operated banana slicer for small scale food processing industries. Research in Agricultural Engineering, 57(4), 144-152.
Spotts, M. F. (1985). Design of machine elements. Englewood Cliffs: Prentice-Hall.
Steger, C., Ulrich, M., & Wiedemann, C. (2018). Machine vision algorithms and applications. John Wiley & Sons.
Teimouri, N., Omid, M., Mollazade, K., Mousazadeh, H., Alimardani, R., & Karstoft, H. (2018). On-line separation and sorting of chicken portions using a robust vision-based intelligent modelling approach. Biosystems Engineering, 167, 8-20.
Tong, J., Xu, S., Chen, D., & Li, M. (2017). Design of a bionic blade for vegetable chopper. Journal of Bionic Engineering, 14(1), 163-171.
Zareiforoush, H. (2014). Design, Development and Evaluation of an Automatic Control System for Rice Whitener Based on Machine Vision and Fuzzy Logic. [Unpublished Ph.D’s thesis]. Faculty of Agriculture. Tarbiat Modares University.
Zhang, B., Huang, W., Li, J., Zhao, C., Fan, S., Wu, J., & Liu, C. (2014). Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review. Food Research International, 62, 326-343.
Zhao, Y., Gong, L., Huang, Y., & Liu, C. (2016). A review of key techniques of vision-based control for harvesting robot. Computers and Electronics in Agriculture, 127, 311-32