Geometric models for analyzing the shape of cauliflower heads
Andrzej Anders
Uniwersytet Warmińsko Mazurski w OlsztynieKrzysztof Jadwisieńczak
Dariusz Choszcz
Abstrakt
Selected geometric properties of cauliflower heads cv. Gohan F1 were analyzed by building numerical models with the use of a 3D scanner. Geometric models of cauliflower heads were developed in ScanStudio HD PRO, FreeCAD, and MeshLab programs. Five geometric models describing the shape of cauliflower heads were generated with the use basic geometric figures and drawing tools in FreeCAD. The geometry of numerical models and geometric models was compared in GOM Inspect. The surface area, volume, and detailed geometric dimensions of the developed models were determined. The deviations in cauliflower dimensions calculated by geometric models were mapped. The surface area, volume, and geometric dimensions of cauliflower heads were most accurately represented by the model generated with the Quadric Edge Collapse Decimation (QECD) function. In this model, the relative error of surface area measurements did not exceed 5%, and the relative error of volume measurements did not exceed 4%. This model was also characterized by the smallest average maximum deviation (+) and the smallest average minimum deviation (-) which was estimated at 8%. The proposed geometric model can be used for research and design purposes.
Słowa kluczowe:
3D scanner, geometric model, solid of revolution, maps of deviationsBibliografia
ANDERS A., MARKOWSKI P., KALINIEWICZ Z. 2015. Numerical modelling of agricultural products on the example of bean and yellow lupine seeds. International Agrophysics, 29(4): 397-403. Google Scholar
ANDUJAR D., RIBEIRO A., QUINTANILLA C.F., DORADO J. 2016. Using depth cameras to extract structural parameters to assess the growth state and yield of cauliflower crops. Computers and Electronics in Agriculture, 122: 67-73. Google Scholar
AZZARI G., GOULDEN M.L., RUSU R.B. 2013. Rapid characterization of vegetation structure with a Microsoft Kinect sensor. Sensors, 13: 2384-2398. Google Scholar
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. Google Scholar
BECERRA L.D., ZULUAGA M., MAYORGA E.Y., MORENO F.L., RUÍZ R.Y., ESCOBAR S. 2022. Cocoa seed transformation under controlled process conditions: Modelling of the mass transfer of organic acids and reducing sugar formation analysis. Food and Bioproducts Processing, 136: 211-225. Google Scholar
BORYGA M., KOŁODZIEJ P. 2022. Reverse Engineering in Modeling Agricultural Products. Agricultural Engineering, 26(1): 105-117. Google Scholar
CIGNONI P., CALLIERI M., CORSINI M., DELLEPIANE M., GANOVELLI F., RANZUGLIA G. 2008. MeshLab: an Open-Source Mesh Processing Tool Sixth Eurographics. Italian Chapter Conference. http://dx.doi.org/10.2312/LocalChapterEvents/ItalChap/ItalianChapConf2008/129-136 Google Scholar
CROCOMBE J.P., LOVATT S.J., CLARKE R.D. 1999. Evaluation of chilling time shape factors through the use of three-dimensional surface modeling. Proceedings of 20th International Congress of Refrigeration, IIR/IIF, Sydney (Paper 353). Google Scholar
DATTA A.K., HALDER A. 2008. Status of food process modeling and where do we go from here (synthesis of the outcome from brainstorming). Comprehensive Reviews in Food Science and Food Safety, 7: 117-120. Google Scholar
ERDOGDU F., BALABAN M.O., CHAU K.V. 1998. Modeling of heat conduction in elliptical cross-section: II. Adaptation to thermal processing of shrimp. Journal of Food Engineering, 38: 241-258. Google Scholar
FLORKIEWICZ A., FILIPIAK-FLORKIEWICZ A., TOPOLSKA K., CIEŚLIK E., KOSTOGRYS R.B. 2014. The effect of technological processing on the chemical composition of cauliflower. Italian Journal of Food Science, 26: 275-281. Google Scholar
FRĄCZEK J., WRÓBEL M. 2006. Methodic aspects of seed shape assessment. Inżynieria Rolnicza, 12(87): 155-163. Google Scholar
FreeCAD 0.20.2. 2023. https://www.freecadweb.org. Google Scholar
GASTÓN A.L., ABALONE R.M., GINER S.A. 2002. Wheat drying kinetics. Diffusivities for sphere and ellipsoid by finite elements. Journal of Food Engineering, 52(4): 313-322. Google Scholar
GOM Inspect. 2023. https://www.gom.com. Google Scholar
GONI S.M., PURLIS E., SALVADORI V.O. 2007. Three-dimensional reconstruction of irregular foodstuffs. Journal of Food Engineering, 82: 536–547. Google Scholar
GONI S.M., PURLIS E., SALVADORI V.O. 2008. Geometry modeling of food materials from magnetic resonance imaging. Journal of Food Engineering, 88: 561–567. Google Scholar
JADWISIEŃCZAK K., KALINIEWICZ Z. 2011. Analysis of the mustard seeds cleaning process. Part 1. Physical properties of seeds. Inżynieria Rolnicza, 9(134): 57-64. Google Scholar
JANCSOK P.T., 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 modeling. Postharvest Biology and Technology, 23: 1-12. Google Scholar
JIAN X., XIAOMING W., ZHENBANG Z., WEIBIN W. 2020. Discrete element modeling and simulation of soybean seed using multi-spheres and super-ellipsoids. IEEE Access, 8: 222672-222683. Google Scholar
JIANGANG L., XIANGMING X., YONGHUAI L., ZEXI R., MELVYN L. SMITH, LIPING J., BO L. 2021. Quantitative potato tuber phenotyping by 3D imaging. Biosystems Engineering, 210: 48-59. Google Scholar
KIM J., MOREIRA R.G., HUANG Y., CASTELL-PEREZ M.E. 2007. 3-D dose distributions for optimum radiation treatment planning of complex foods. Journal of Food Engineering, 79: 312–321. Google Scholar
LONG Z., JIANQUN Y., YANG W., DONGXU Y., YAJUN Y. 2020. A study on the modelling method of maize-seed particles based on the discrete element method. Powder Technology, 374: 353-376. Google Scholar
MeshLab Visual Computing Lab – ISTI – CNR. 2013. http://meshlab.sourceforge.net. Google Scholar
MIESZKALSKI L. 2013. Computer-aiding of mathematical modeling of the carrot (Daucus carota L.) root shape. Annals of Warsaw University of Life Sciences – SGGW. Agriculture, 61: 17-23. Google Scholar
NASRINA T.A.A., YASMINB L., ARFINA M.S., RAHMANA MD. A., MOLLAC M.M., SABUZ A.A., AFROZ M. 2022. Preservation of postharvest quality of fresh cut cauliflower through simple and easy packaging techniques. Applied Food Research, 2: 1-12. Google Scholar
NextEngine User Manual. 2010. http://www.nextengine.com. Google Scholar
OLESEN J.E., GREVSEN K. 1997. Effects of temperature and irradiance on vegetative growth of cauliflower (Brassica oleracea L. botrytis) and broccoli (Brassica oleracea L. italic). Journal of Experimental Botany, 48: 1591-1598. Google Scholar
RAHMI U., FERRUH E. 2009. Potential use of 3-dimensional scanners for food process modeling. Journal of Food Engineering, 93: 337-343. Google Scholar
SABLIOV C.M., BOLDER D., KEENER K.M., FARKAS B.E. 2002. Image processing method to determine surface area and volume of axi-symmetric agricultural products. International Journal of Food Properties, 5: 641-653. Google Scholar
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. Google Scholar
SHUAI W., ZHIHONG Y., AORIGELE, WENJIE Z. 2022. Study on the modeling method of sunflower seed particles based on the discrete element method. Computers and Electronics in Agriculture, 198: 1-16. Google Scholar
SINNOTT M.D., HARRISON SM., CLEARY P.W. 2021. A particle-based modelling approach to food processing operations. Food and Bioproducts Processing, 127: 14-57. Google Scholar
SIRIPON K., TANSAKUL A., MITTAL G.S. 2007. Heat transfer modeling of chicken cooking in hot water. Food Research International, 40: 923-930. Google Scholar
SZWEDZIAK K., RUT J. 2008. Assessment of pollutants of the grain corn with the help of computer analysis of the image. Postępy Techniki Przetwórstwa Spożywczego, 1: 14-15. Google Scholar
THAKUR A., BANERJEE A.G., GUPTA S.K. 2009. A survey of CAD model simplification techniques for physics-based simulation applications. Computer-Aided Design, 41: 65-80. Google Scholar
VERBOVEN P., DE BAERDEMAEKER J., NICOLAI B.M. 2004. Using computational fluid dynamics to optimize thermal processes. Richardson. P. (Ed.), Improving the Thermal Processing of Foods. CRC Press. Boca Raton, FL, p. 82-102. Google Scholar
Uniwersytet Warmińsko Mazurski w Olsztynie