Abstract
Konya ili Kasınhanı ilçesi, Türkiye'nin en büyük havuç üretimine sahiptir. 2017 yılında Konya ili havuç üretim alanlarının yaklaşık% 46.5'ine ve toplam üretimin% 59.7'sine sahiptir. Bölgede birkaç yıkama ve paketleme tesisi bulunmaktadır. Bu tesisler tamamen benzer özellikler sergilemekte vei bölgenin ihtiyaçlarını tam olarak karşılamaktadır. Yıkama havuzlarından gelen havuçlar önce mekanik sınıflandırma makinelerine, daha sonra paketleme bölümüne veya bazı tesislerde yıkamadan direk paketleme bölümüne gelir. Sınıflandırma ve paketleme işlemleri bu tesislerde elle yapılır. Mekanik sınıflandırma makinelerinin sınıflandırma verimliliğinin yetersiz olduğu bilinmektedir. Bu çalışmada, gerçek zamanlı görüntü işleme makinesinin mekanik, elektronik ve yazılım bölümleri açıklanmıştır. Sistem bir bant konveyörü, kameralar ve görüntüleri almak için kapalı odadan, görüntü işleme ve kontrol bilgisayarı ve servo motorlara bağlı yönlendirme kapaklarından oluşmuştur. Deneyler sonucunda, gerçek zamanlı görüntü işleme makinesinde havuç sınıflandırma oranları % 80.14 ile % 100 arasında değişmektedir.
Project Number
1110123
References
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- Piedad E.Jr, Larada J.I., Pojas G.J., Ferrer L.V.V (2018). Postharvest classification of banana (Musa acuminata) using tier-based machine learning, Postharvest Biology and Technology, 145, 93-100. doi:/10.1016/j.postharvbio.2018.06.004
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- Zhang, B., Huanga,W., Lia,J., Zhaoa,C., Fana,S., Wua, J., Liub,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. dx.doi.org/10.1016/j.foodres.2014.03.012
- Zhang B., Huang W., Gong L., Li J., Zhao C., Liu C., Huang D. (2015). Computer vision detection of defective apples using automatic lightness correction and weighted RVM classifier, Journal of Food Engineering, Vol. 146, 143-151. doi.org/10.1016/j.jfoodeng.2014.08.024
Abstract
Kasınhanı district of Konya province has the greatest carrot production in Turkey. By the year 2017, Konya Province has approximately 46.5% of carrot production areas and 59.7% of total production. There are several washing and packing facilities in the region. These facilities show totally similar features and fully satisfy the needs of the region. Carrots coming from the washing pools come firstly to the mechanical grading machines and then to the packing department or directly to the packing department in some facilities. Grading and packing processes are carried out manually in these facilities. The classification efficiency of mechanical classification machines is known to be insufficient. In this study, mechanical, electronic and software sections of the real-time image processing machine are explained. The system was composed of a belt conveyor, cameras and closed chamber to receive images, image processing and control computer and routing covers attached to servo motors. As a result of the experiments, carrot classification rates ranged from 80.14 to 100% in real-time image processing machine.
Supporting Institution
Selçuk Üniversity Scientific Research Projects Department
Project Number
1110123
References
- Anonymous, 2017, http://faostat.fao.org/site/567/DesktopDefault.aspx [Last Accessed:02.02.2017]
- Anonymous, 2019, https://biruni.tuik.gov.tr/medas/?kn=104&locale=tr [Last Accessed:14.01.2019]
- Bradski G., Kaehler A. (2008). Learning OpenCV, Repkover, United States of America, ISBN: 978-0-596-51613-0.
- Clement, J., N. Novas, J. A. Gazquez, F. Manzano-Agugliaro (2013). An active contour computer algorithm for the classification of cucumbers, Computers and Electronics in Agriculture, 92, 75–81. doi.org/10.1016/j.compag.2013.01.006.
- Donis-Gonzáleza I.R., Guyera D.E, Pease A. (2016). Postharvest noninvasive assessment of undesirable fibrous tissue in fresh processing carrots using computer tomography images, Journal of Food Engineering, 190, 154-166. doi:/10.1016/j.jfoodeng.2016.06.024
- Feng, G., Qixin, C., (2004). Study on Color Image Processing Based Intelligent Fruit Sorting System, Proceedings of the 5th World Congress on Intelligent Control and Automation, 6, 4802-4805, China
- Gonzalez R. C., Woods R. E. (2008). Digital Image Processing, Pearson International Edition, Pearson Prentice Hall, United States of America, ISBN: 0-13-168728-x 978-0-13-168728-8.
- Hetal N. Patel, R.K.Jain (2012). On-Line Quality Assessment Of Horticultural Products Using Machine Vision, International Journal Of Scientific & Technology Research Volume 1, Issue 9, ISSN:2277-8616
- Jhawar J. (2016). Orange Sorting by Applying Pattern Recognition on Colour Image, Procedia Computer Science 78, 691- 697. doi: 10.1016/j.procs.2016.02.118
- Liming, X., Z. Yanchao (2010). Automated strawberry grading system based on image processing, Computers and Electronics in Agriculture,71, S32-S39. doi.org/10.1016/j.compag.2009.09.013
- Moallem P., Serajoddin A., Pourghassem H. (2017). Computer vision-based apple grading for golden delicious apples based on surface features, Information Processing in Agriculture, 4(1),33-40. doi:/10.1016/j.inpa.2016.10.003
- Ostrozlik, M. (1990). Analysis Carrot of Belgium Sorting. Zemedelska-Technika. 36(5), 277-284
- Örnek, M.N., (2014). Havuç sınıflandırmada gerçek zamanlı görüntü işleme makinesi tasarımı ve bazı mekanik sınıflandırma makineleri ile boylama etkinliklerinin karşılaştırılması, Selçuk Üniversitesi, Fen Bilimleri Enstitüsü, Tarım Makineleri Anabilim Dalı, Konya
- Piedad E.Jr, Larada J.I., Pojas G.J., Ferrer L.V.V (2018). Postharvest classification of banana (Musa acuminata) using tier-based machine learning, Postharvest Biology and Technology, 145, 93-100. doi:/10.1016/j.postharvbio.2018.06.004
- Sofu, M.M., O. Er, M.C. Kayacan, B. Cetişli B. (2013). Elmaların Görüntü İşleme Yöntemi ile Sınıflandırılması ve Leke Tespiti, Gıda Teknolojileri Elektronik Dergisi 8 (1), 12- 25
- 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. doi.org/10.1016/j.compag.2016.06.030
- Vursavuş, K.K., Özgüven, F., (2008). Modeling the Mass of Oranges by Geometrical Properties, 10th International Congress on Mechanization and Energy in Agriculture, 14-17 October, 745-751, Antalya
- Xiao-bo, Z., Jie-wen, Z., Yanxiao, L., Holmes, M. (2010). In-line detection of apple defects using three color cameras system, Computers and Electronics in Agriculture, 70, 129-134. doi.org/10.1016/j.compag.2009.09.014.
- Zhang, B., Huanga,W., Lia,J., Zhaoa,C., Fana,S., Wua, J., Liub,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. dx.doi.org/10.1016/j.foodres.2014.03.012
- Zhang B., Huang W., Gong L., Li J., Zhao C., Liu C., Huang D. (2015). Computer vision detection of defective apples using automatic lightness correction and weighted RVM classifier, Journal of Food Engineering, Vol. 146, 143-151. doi.org/10.1016/j.jfoodeng.2014.08.024
Details
| Primary Language | English |
|---|---|
| Subjects | Agricultural Engineering, Agricultural Engineering (Other) |
| Journal Section | Articles |
| Authors | |
| Project Number | 1110123 |
| Publication Date | June 30, 2020 |
| Acceptance Date | May 6, 2020 |
| Published in Issue | Year 2020 Volume: 30 Issue: 2 |
Cited By
