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Tytuł artykułu

A mathematical method for modeling the shape of apples. Part 2. Calculation and validation of results

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The aim of the study was to propose a mathematical method for modeling the shape of apples cv. Ligol and their anatomical parts, the locule and the pericarp, with the use of Bézier curves. The method developed in part 1 of the study was used to generate 3D models describing the shape of an apple, the locule and the pericarp. The main projection planes of the apple, locule and pericarp were compared with their corresponding models to reveal that the proposed method supports modeling of geometric solids with sufficient accuracy for practical applications.
Słowa kluczowe
Twórcy
  • Department of Production Management and Engineering, Warsaw University of Life Sciences, Nowoursynowska 166, 02-787 Warsaw, Poland
autor
  • Department of Production Management and Engineering, Warsaw University of Life Sciences, Nowoursynowska 166, 02-787 Warsaw, Poland
Bibliografia
  • 1. Mieszkalski L. & Wojdalski J., 2017. A mathematical method for modeling the shape of apple. Part 1. description of the method. ECONTECHMOD. An International Quarterly Journal.
  • 2. Kim G-W. Do G-S. Bae Y. & Sagara Y., 2008. Analysis of Mechanical Properties of Whole Apple Using Finite Element Method Based on Three-Dimensional Real Geometry. Food Sci. Technol. Res., 14 (4), 329-336.
  • 3. Anand A. & Scanlon M. G.,2002. Dimensional effects on the prediction of texture-related mechanical properties of foods by indentation. Trans. of the ASAE, 45, 1045-1050.
  • 4. Bartoň S. Severa L. & Buchar J., 2010. New algorithm for biological objects’ shape evaluation and data reduction. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, LVIII 1, 1, 13-20.
  • 5. Bartoň S., 2008. Three dimensional modelling of the peach in Maple. In: CHLEBOUN, J. Programs and Algorithms of numerical Mathematics. 1st ed. Praha. Matematickýústav AV ČR; 7-14. ISBN 978-80-85823-55-4.
  • 6. Rogge S. Beyene S. Herremans E. Defraeye T. Verboven P. & Nicolai B., 2013. A geometrical model generator for quasi-axisymmetric fruit. Proceedings of the 7th International Conference on Functional-Structural Plant Models, Saariselkä, Finland, 9-14 ,June 2013. Eds. Risto Sievänen, Eero Nikinmaa, Christophe Godin, Anna Lintunen & Pekka Nygren. 92-94. http://www.metla.fi/fspm 2013/proceedings. ISBN 978-951-651-408-9.
  • 7. Mieszkalski L., 2015. Komputerowe wspomaganie modelowania kształtu jabłek. Acta Sci. Pol. Technica Agraria, 14(3-4), 19-31.
  • 8. Mendoza F., Verboven P., Ho Q. T., Mebatsion H. K., Nguyen T. A., Wevers M. & Nicolaï B., 2006. 3-D microscale geometry of apple tissue using X-ray computed microtomography. IUFoST 2006; 761-773, DOI: 10.1051/IUFoST:20060431.
  • 9. Jancsók P. Coucke P. Beuselinck A. De Baerdemaeker J.& Nicolai B., 1998. 3D finite element model generation by computer vision for the modal analysis of fruits. Food quality modeling. Edited by: B. M. Nicolai, J. De Baerdemaeker; 139-144, ISBN 92-828-3309-7.
  • 10. Romo E. R., 2005. An experimental, analytical and numerical analysis of apple bruising. bibing.us. es/proyectos/abreproy/3994/fichero/Thesis.pd. Data dostępu 05.12.2016.
  • 11. Uyar R. & Erdoğdu F., 2016. Computational Modelling of Heat Transfer in Food Processes with 3-Dimensional Scanners [www.icef11.org/content/ papers/mcf/MCF280.pdf]. Data dostępu 05.12.2016.
  • 12. Bubeníčková A. Simeonovová J. Kumbár V. Jůzl M. & Nedomová Š., 2011. Mathematical descriptive characteristics of potato tubers’ shape. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, LIX 8, 6, 63 - 67.
  • 13. Costa, C., Antonucci, F., Pallattino, F., Aguzzi, J., Sun, D. W., & Menesatti, P. 2011. Shape analysis of agricultural products: A review of recent research advances and potential application to computer vision. Food & Bioprocess Technology, 4, 673-692.
  • 14. Prabha D. S. & Kumar J. S., 2012. A study on image processing methods for fruit classification. Proc. Int. Conf. on Computational Intelligence and Information Technology, CIIT, 403-406.
  • 15. Ohali Y. Al., 2011. Computer vision based date fruit grading system: Design and implementation. Journal of King Saud University – Computer and Information Sciences, 23, 1, 29-36.
  • 16. Kavdır I. & Guyer D. E., 2008. Evaluation of different pattern recognition techniques for apple sorting. Biosystems Engineering, 99, 2, 211–219.
  • 17. Mehla P. M., Chenb Y-R., Kimb M. S., & Chan D. E., 2004. Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations. Journal of Food Engineering, 61, 1, 67-81.
  • 18. Ibrahim M. F., Sa’ad F. S. A., Zakaria A. & MdShakaff A. Y., 2016. In-Line Sorting of Harumanis Mango Based on External Quality Using Visible Imaging. Sensors 2016, 16, 1753; 2-17 doi:10.3390/s16111753, http://www.mdpi.com/journal/sensors.
  • 19. Czernyszewicz E., 2008. The importance of some selected qualitative features of apples for buyers (Ważność wybranych cech jakościowych jabłek dla konsumentów). Żywność. Nauka. Technologia. Jakość, 1 (56), 114-125.
  • 20. Flys I., 2014. Conception of simulating the processes of innovative projects initialization for agro-industrial production in Ukraine. ECONTECHMOD. An International Quarterly Journal, 3, 4, 75-81.
  • 21. Gołacki K. Bobin G. & Stropek Z., 2009.Bruise resistance of Apple (Melrose variety). TEKA Kom. Mot. Energ. Roln. – OL PAN, 9, 40-47.
  • 22. Puchalski C. Gorzelany J. Zaguła G.& Brusewitz G., 2008. Image analysis for apple defect detection. TEKA Kom. Mot. Energ. Roln. –OL PAN, 8, 197-205.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-913daf17-f021-4abd-b781-4da0964fa698
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