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

Document Type : Research Paper

Authors

1 Biosystems Engineering Department, Tarbiat Modares University, Tehran, Iran

2 Professor of biosystems engineering Tarbiat Modares Univ.

3 Biosystems Engineering Dept., Agricultural Faculty, Tarbiat Modares University

Abstract

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.

Keywords

References

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
Iranian Journal of Biosystems Engineering
Volume 52, Issue 2
June 2021
Pages 301-309

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History

  • Receive Date: 08 June 2020
  • Revise Date: 15 August 2020
  • Accept Date: 22 August 2020

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