A mathematical method for modeling the shape of apples. Part 1. Description of the method

Mieszkalski, L.; Wojdalski, J.

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Tytuł artykułu

A mathematical method for modeling the shape of apples. Part 1. Description of the method

Autorzy

Mieszkalski L. , Wojdalski J.

Treść / Zawartość

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Abstrakty

This study proposes a mathematical method for modeling the shape of apples, locules and pericarps with the use of Bézier curves. The concave and convex parts of apples cv. Ligol were described with three smoothly-joined Bézier curves. Contours were described based on images of an apple rotated at intervals of 36° relative to its natural axis of symmetry. A 3D model was formed by Bézier curves positioned along the apple’s meridians. The shape of the locule and the pericarp was described with the use of two smoothly-joined Bézier curves each, rotated relative to the apple’s longitudinal axis.

Słowa kluczowe

apple cv. Ligol shape locule pericarp Bézier curves method mathematical model

Wydawca

Polish Academy of Sciences, Branch in Lublin

Czasopismo

ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes

Rocznik

2017

Tom

Vol. 6, No 3

Strony

97--104

Opis fizyczny

Bibliogr. 48 poz., rys., wykr., wz.

Twórcy

autor

Mieszkalski L.

leszek_mieszkalski@sggw.pl

Department of Production Management and Engineering, Warsaw University of Life Sciences, Nowoursynowska 166, 02-787 Warsaw, Poland

autor

Wojdalski J.

janusz_wojdalski@sggw.pl

Department of Production Management and Engineering, Warsaw University of Life Sciences, Nowoursynowska 166, 02-787 Warsaw, Poland

Bibliografia

1. Abbott J. A., 1999. Quality measurement of fruits and vegetables. Postharvest Biology and Technology, 15(3), 207-225.
2. Anderson E. R. Cuccia, D. J. & Durkin A. J. 2007, February. Detection of bruises on Golden Delicious apples using spatial-frequency-domain imaging. In Biomedical Optics (BiOS) 2007 (pp. 64301O-64301O). International Society for Optics and Photonics.
3. Blasco J. Aleixos N. Gómez-Sanchis J. & Moltó E., 2009. Recognition and classification of external skin damage in citrus fruits using multispectral data and morphological features. Biosystems Engineering, 103(2), 137-145.
4. Chen Y. R. Chao, K. & Kim M. S. 2002. Machine vision technology for agricultural applications. Computers and Electronics in Agriculture, 36(2), 173-191.
5. 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.
6. Du C. J. & Sun D. W., 2004. Recent developments in the applications of image processing techniques for food quality evaluation. Trends in Food Science & Technology, 15(5), 230-249.
7. Golpira H. & Golpîra H., 2013. Application of signal processing technique for the modification of a fruit sorting machine. International Journal of Advanced Mechatronic Systems, 5(2), 122-128.
8. Gołacki K. & Kołodziej P., 2011. Impact testing of biological material on the example of apple tissue. TEKA Kom. Mot. i Energ. Roln. – OL PAN, 11c, 74-82
9. Kavdır I. & Guyer D. E., 2004. Comparison of artificial neural networks and statistical classifiers in apple sorting using textural features. Biosystems Engineering, 89(3), 331-344.
10. Leemans V. & Destain M. F., 2004. A real-time grading method of apples based on features extracted from defects. Journal of Food Engineering, 61(1), 83-89.
11. López-García F. Andreu-García G. Blasco J. Aleixos N. & Valiente J. M., 2010. Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach. Computers and Electronics in Agriculture, 71(2), 189-197.
12. Mebatsion H. K. Verboven P. Ho Q. T. Verlinden B. E. & Nicolaï B. M., 2008. Modelling fruit (micro) structures, why and how?. Trends in food science & technology, 19(2), 59-66.
13. Pakrashi V. O'Connor A. J. & Schoefs F., 2007, July. An image analysis based damage classification methodology. In Applications of Statistics and Probability in Civil Engineering: Proceedings of the 10th International Conference, ICASP10, Tokyo, Japan, 31 July-3 August 2007. Taylor & Francis.
14. Pieczywek P. M. & Zdunek A., 2012. Automatic classification of cells and intercellular spaces of apple tissue. Computers and Electronics in Agriculture, 81, 72-78.
15. Shahin M. A. Tollner E. W. McClendon R. W. & Arabnia H. R., 2002. Apple classification based on surface bruises using image processing and neural networks. Transactions of the ASAE, 45(5), 1619.
16. Unay D. & Gosselin, B., 2005, September. Artificial neural network-based segmentation and apple grading by machine vision. In IEEE International Conference on Image Processing 2005 (Vol. 2, pp. II-630). IEEE.
17. Unay D. & Gosselin B., 2006. Automatic defect segmentation of ‘Jonagold’apples on multi-spectral images: A comparative study. Postharvest Biology and Technology, 42(3), 271-279.
18. Aregawi W. A. Abera M. K. Fanta S. W. Verboven, P. & Nicolai B., 2014. Prediction of water loss and viscoelastic deformation of apple tissue using a multiscale model. Journal of Physics: Condensed Matter, 26(46), 464111.
19. Goñi S. M. & Purlis E., 2010. Geometric modelling of heterogeneous and complex foods. Journal of Food Engineering, 4, 97, 547-554.
20. Ho Q. T. Carmeliet J. Datta A. K. Defraeye T. Delele M. A. Herremans E. & Van Liedekerke P., 2013. Multiscale modeling in food engineering. Journal of Food Engineering, 114(3), 279-291.
21. Balcerzak K. Weres J. Górna K. & Idziaszek P., 2015. Modeling of agri-food products on the basis of solid geometry with examples in Autodesk 3DS Max and finite element mesh generation. Journal of Research and Applications in Agricultural Engineering, 60(2), 5-8.
22. Weres J. Olek W. Kujawa S. & Siatkowski M., 2014a. Integration of software components for determination and analysis of properties of agri-food and forest products. Journal of Research and Applications in Agricultural Engineering, 59(1), 159-163.
23. Weres J. Kiecana M. & Balcerzak K., 2014b. Two approaches to representing agri-food product geometry – an original software for constructing finite element models and the 3ds max approach. Journal of Research and Applications in Agricultural Engineering, 59(1), 155-158.
24. Anders A. Markowski P. & Kaliniewicz Z., 2014. Badanie właściwości geometrycznych i fizycznych owoców wybranych odmian gruszy na podstawie modeli numerycznych uzyskanych za pomocą skanera 3D. Zeszyty Problemowe Postępów Nauk Rolniczych, 577, 3-12.
25. Gielis J., 2003. A generic geometric transformation that unifies a wide range of natural, and abstract shapes. American Journal of Botany, 90(3), 333-338.
26. Gielis J. & Gerats T., 2004. A botanical perspective on modeling plants and plant shapes in computer graphics. International Conference on Computer, Communication and Control Technologies. Austin, Texas.
27. Goñi S. M. Purlis E. & Salvadori V. O., 2007. Three-dimensional reconstruction of irregular foodstuffs. Journal of Food Engineering 82, 536-547.
28. Jancsók P. Clijmans L. Nicolai B. M. & De Baerdemaeker J., 2001. Investigation of the effect of shape on the acoustic response of ‘Conference’ pears by finite element modelling. Postharvest Biology and Technology 23, 1-12.
29. Kafashan J., Tijskens B. Moshou D. Bravo C. De Baerdemaeke J. & Ramon H., 2006. A new method to model a 3D shape of biomaterials for using in post-harvest modelling. Control Applications in Post-Harvest and Processing Technology (CAPPT 2006), 223.
30. 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 Science and Technology Research, 14(4), 329-336.
31. Mieszkalski L., 2014. Mathematical model of the shape of broad seed. Ann. Warsaw Univ. Life Sci. – SGGW, Agricult. 63, 41-48.
32. Scheerlinck N. Marquenie D. Jancsok P. T. Verboven P. Moles C. G. Banga J. R. & Nicolai B. M., 2004. A model-based approach to develop periodic thermal treatments for surface decontamination of strawberries. Postharvest Biology and Technology, 34, 39-52.
33. 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.
34. Costa C. Antonucci F., Pallottino 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 and Bioprocess Technology, 4(5), 673-692.
35. Cubero S. Aleixos N. Moltó E. Gómez-Sanchis J. & Blasco J., 2011. Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables. Food and Bioprocess Technology, 4(4), 487-504.
36. Davies E. R., 2009. The application of machine vision to food and agriculture: a review. The Imaging Science Journal, 57(4), 197-217.
37. Rosell J. R. & Sanz R., 2012. A review of methods and applications of the geometric characterization of tree crops in agricultural activities. Computers and Electronics in Agriculture, 81, 124-141.
38. Kleynen O. Leemans V. & Destain M. F., 2005. Development of a multi-spectral vision system for the detection of defects on apples. Journal of Food Engineering, 69(1), 41-49.
39. Kodagali J. A. & Balaji S., 2012. Computer vision and image analysis based techniques for automatic characterization of fruits-a review. International Journal of Computer Applications, 50(6), 6-12.
40. Mahendran R. Jayashree G. C. & Alagusundaram K., 2012. Application of Computer Vision Technique on Sorting and Grading of Fruits and Vegetables. J Food Process & Technology S1-001. doi:10.4172/2157-7110.S1-001
41. Moreda G. P. Muñoz M. A. Ruiz-Altisent M. & Perdigones A., 2012. Shape determination of horticultural produce using two-dimensional computer vision–A review. Journal of Food Engineering, 108(2), 245-261.
42. Narendra V. G. & Hareesha K. S., 2010. Quality inspection and grading of agricultural and food products by computer vision - A Review. International Journal of Computer Applications, 2(1), 43-65.
43. Patel, K. K., Kar, A., Jha, S. N., & Khan, M. A., 2012. Machine vision system: a tool for quality inspection of food and agricultural products. Journal of Food Science and Technology, 49(2), 123-141.
44. Foley J. D. Van Dam A. Feiner S.K. Hughes J.F. & Phillips R. L., 2001. Wprowadzenie do grafiki komputerowej. WNT, Warszawa. ISBN 83-204-2662-6.
45. Kiciak P., 2000. Podstawy modelowania krzywych i powierzchni. Zastosowania w grafice komputerowej. WNT, Warszawa. ISBN 83-204-2464-X.
46. Opera J., 2002. Geometria różniczkowa i jej zastosowanie. Wydawnictwo Naukowe PWN, Warszawa. ISBN 83-01-13875-0.
47. Mieszkalski L., 2015. Komputerowe wspomaganie modelowania kształtu jabłek. Acta Sci. Pol. Technica Agraria, 14(3-4), 19-31.
48. Hongbo S., Dai Shiming G. X., Shenglian L. & Yanan W., 2008. Geometry Modeling and Visualization of the Apple Fruit. Chinese Agricultural Science Bulletin, 10, 116.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).

Typ dokumentu

Bibliografia

Identyfikator YADDA

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