Food and Bioprocess Technology

, Volume 10, Issue 11, pp 2100–2112 | Cite as

Building 3D Statistical Shape Models of Horticultural Products

  • Femke Danckaers
  • Toon Huysmans
  • Mattias Van Dael
  • Pieter Verboven
  • Bart Nicolaï
  • Jan Sijbers
Original Paper


A method to build a 3D statistical shape model of horticultural products is described. The framework consists of two parts. First, the surfaces of the horticultural products, which are extracted from X-ray CT scans, are registered to obtain meaningful correspondences between the surfaces. In the second part, a statistical shape model is built from these corresponded surfaces, which maps out the variability of the surfaces and allows to generate new, realistic surfaces. The proposed shape modelling method is applied to 30 Jonagold apples, 30 bell peppers, and 52 zucchini. The average geometric registration error between the original instance and the deformed reference instance is 0.015 ± 0.011 m m for the apple dataset, 0.106 ± 0.026 m m for the bell pepper dataset, and 0.027 ± 0.007 m m for the Zucchini dataset. All shape models are shown to be an excellent representation of their specific population, as they are compact and able to generalize to an unseen sample of the population.


Statistical shape model Parameterization Apple Zucchini Bell pepper 



This work was supported by the Agency for Innovation by Science and Technology in Flanders (IWT SB 141520 and IWT SBO 120033 TomFood).


  1. Amberg, B, Romdhani, S, & Vetter, T (2007). Optimal step nonrigid ICP algorithms for surface registration. In Conference on computer vision and pattern recognition (pp. 1–8). IEEE.Google Scholar
  2. Barnea, E, Mairon, R, & Ben-Shahar, O (2016). Colour-agnostic shape-based 3D fruit detection for crop harvesting robots. Biosystems Engineering.Google Scholar
  3. Blanc, R, Seiler, C, Székely, G, Nolte, L P, & Reyes, M (2012). Statistical model based shape prediction from a combination of direct observations and various surrogates: application to orthopaedic research. Medical Image Analysis, 16(6), 1156–1166.CrossRefGoogle Scholar
  4. Borsa, J, Chu, R, Sun, J, Linton, N, & Hunter, C (2002). Use of CT scans and treatment planning software for validation of the dose component of food irradiation protocols. Radiation Physics and Chemistry, 63 (3–6), 271–275.CrossRefGoogle Scholar
  5. Bruse, J L, McLeod, K, Biglino, G, Ntsinjana, H N, Capelli, C, Hsia, T Y, Sermesant, M, Pennec, X, Taylor, A M, & Schievano, S (2016). A statistical shape modelling framework to extract 3d shape biomarkers from medical imaging data: assessing arch morphology of repaired coarctation of the aorta. BMC Medical Imaging, 16(1), 40.CrossRefGoogle Scholar
  6. Cignoni, P, Callieri, M, Corsini, M, Dellepiane, M, Ganovelli, F, & Ranzuglia, G (2008). Meshlab: an open-source mesh processing tool. Eurographics (pp. 129–136). Italy.Google Scholar
  7. Cootes, T, Taylor, C, Cooper, D, & Graham, J (1995). Active shape models—their training and application. Computer Vision and Image Understanding, 61(1), 38–59.CrossRefGoogle Scholar
  8. Corsini, M, Cignoni, P, & Scopigno, R (2012). Efficient and flexible sampling with blue noise properties of triangular meshes. IEEE Transactions on Visualization and Computer Graphics, 18(6), 914–924.CrossRefGoogle Scholar
  9. 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.CrossRefGoogle Scholar
  10. Crum, W R, Danckaers, F, Huysmans, T, Cotel, M C, Natesan, S, Modo, M M, Sijbers, J, Williams, S C R, Kapur, S, Vernon, A C, & et al (2016). Chronic exposure to haloperidol and olanzapine leads to common and divergent shape changes in the rat hippocampus in the absence of grey-matter volume loss. Psychological Medicine, 46(15), 3081–3093.CrossRefGoogle Scholar
  11. Danckaers, F, Huysmans, T, Lacko, D, Ledda, A, Verwulgen, S, Van Dongen, S, & Sijbers, J (2014). Correspondence preserving elastic surface registration with shape model prior. In ICPR’14 (pp. 2143–2148). IEEE.Google Scholar
  12. Davies, R H (2002). Learning shape: optimal models for analysing natural variability. PhD thesis, University of Manchester.Google Scholar
  13. Dehghannya, J, Ngadi, M, & Vigneault, C (2010). Mathematical modeling procedures for airflow, heat and mass transfer during forced convection cooling of produce: a review. Food Engineering Reviews, 2(4), 227–243.CrossRefGoogle Scholar
  14. Delele, M, Tijskens, E, Atalay, Y, Ho, Q, Ramon, H, Nicolaï, B, & Verboven, P (2008). Combined discrete element and CFD modelling of airflow through random stacking of horticultural products in vented boxes. Journal of Food Engineering, 89(1), 33– 41.CrossRefGoogle Scholar
  15. Dijck, C V, Wirix-Speetjens, R, Huysmans, T, Danckaers, F, Sijbers, J, & Vander Sloten, J (2014). Influence of correspondence method on statistical model based shape prediction. In IEEE International symposium on biomedical imaging (ISBI).Google Scholar
  16. Dryden, I L, & Mardia, K V. (1998). Statistical shape analysis wiley series in probability and statistics. Chichester: Wiley.Google Scholar
  17. Goni, S M, Purlis, E, & Salvadori, V O (2007). Three-dimensional reconstruction of irregular foodstuffs. Journal of Food Engineering, 82(4), 536–547.CrossRefGoogle Scholar
  18. Goni, S M, Purlis, E, & Salvadori, V O (2008). Geometry modelling of food materials from magnetic resonance imaging. Journal of Food Engineering, 88(4), 561–567.CrossRefGoogle Scholar
  19. Gower, J C (1975). Generalized procrustes analysis. Psychometrika, 40(1), 33–51.CrossRefGoogle Scholar
  20. Ho, Q T, Verboven, P, Verlinden, B E, Herremans, E, Wevers, M, Carmeliet, J, & Nicolaï, B M (2011). A three-dimensional multiscale model for gas exchange in fruit. Plant Physiology, 155(3), 1158–1168.CrossRefGoogle Scholar
  21. Huysmans, T, Sijbers, J, & Verdonk, B (2005). Parameterization of tubular surfaces on the cylinder. Journal of the Winter School of Computer Graphics, 10(3), 97–104.Google Scholar
  22. Iqbal, S M, Gopal, A, & Sarma, A S V (2011). Volume estimation of apple fruits using image processing. In International conference on image information processing (ICIIP) (pp. 1–6). IEEE.Google Scholar
  23. Jancsok, P T, Clijmans, L, Nicolaï, 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), 1–12.CrossRefGoogle Scholar
  24. Kendall, D G (1989). A survey of the statistical theory of shape. Statistical Science, 4(2), 87–99.CrossRefGoogle Scholar
  25. Kim, J, Moreira, R, Huang, Y, & Castell-Perez, M (2007). 3-D dose distributions for optimum radiation treatment planning of complex foods. Journal of Food Engineering, 79(1), 312–321.CrossRefGoogle Scholar
  26. Ling, L, Hongzhen, X, Wenlin, S, & Gelin, L (2007). Research on visualisation of fruits based on deformation. New Zealand Journal of Agricultural Research, 50(5), 593–600.CrossRefGoogle Scholar
  27. Mebatsion, H, Boudon, F, Godin, C, Pradal, C, Gėnard, M, Goz-Bac, C, & Bertin, N (2011). A novel profile based model for virtual representation of quasi-symmetric plant organs. Computers and Electronics in Agriculture, 75(1), 113–124.CrossRefGoogle Scholar
  28. Moreda, G, Muṅoz, M, 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.CrossRefGoogle Scholar
  29. Muhammad, G (2015). Date fruits classification using texture descriptors and shape-size features. Engineering Applications of Artificial Intelligence, 37, 361–367.CrossRefGoogle Scholar
  30. Peng, Y, & Lu, R (2006). Improving apple fruit firmness predictions by effective correction of multispectral scattering images. Postharvest Biology and Technology, 41(3), 266–274.CrossRefGoogle Scholar
  31. Rakun, J, Stajnko, D, Berk, P, Lakota, M, & Zazula, D (2012). Detecting natural objects by means of 2D and 3D shape analysis. In Actual tasks on agricultural engineering (pp. 345–354). Croatia.Google Scholar
  32. Rogge, S, Defraeye, T, Herremans, E, Verboven, P, & Nicolaï, B M (2015). A 3D contour based geometrical model generator for complex-shaped horticultural products. Journal of Food Engineering, 157, 24–32.CrossRefGoogle Scholar
  33. Sayinci, B, Kara, M, Erciṡli, S, Duyar, Ö, & Ertu̇rk, Y (2015). Elliptic Fourier analysis for shape distinction of Turkish hazelnut cultivars. Erwerbs-Obstbau, 57(1), 1–11.CrossRefGoogle Scholar
  34. Scheerlinck, N, Marquenie, D, Jancsók, P T, Verboven, P, Moles, C G, Banga, J R, & Nicolaï, B M (2004). A model-based approach to develop periodic thermal treatments for surface decontamination of strawberries. Postharvest Biology and Technology, 34(1), 39–52.CrossRefGoogle Scholar
  35. Schroeder, W, Martin, K, Lorensen, B, & Kitware, I. (2006). The visualization toolkit: an object-oriented approach to 3D graphics. Clifton Park: Kitware.Google Scholar
  36. Soons, JA, Danckaers, F, Keustermans, W, Huysmans, T, Sijbers, J, Casselman, JW, & Dirckx, JJ (2016). 3d morphometric analysis of the human incudomallear complex using clinical cone-beam ct. Hearing Research, 340, 79–88.CrossRefGoogle Scholar
  37. Stajnko, D, Rozman, Ċ, Pavloviċ, M, Beber, M, & Zadravec, P (2013). Modeling of ’Gala’ apple fruits diameter for improving the accuracy of early yield prediction. Scientia Horticulturae, 160, 306–312.CrossRefGoogle Scholar
  38. Tornincasa, S, Bonisoli, E, & Brino, M (2016). Parametric, asymmetric and stochastic-based 3D CAD model of Tonda Gentile Trilobata hazelnut variety. Biosystems Engineering, 144, 72–84.CrossRefGoogle Scholar
  39. Torppa, J, Valkonen, J P T, & Muinonen, K (2007). Three-dimensional stochastic shape modelling for potato tubers. Potato Research, 49(2), 109–118.CrossRefGoogle Scholar
  40. Verboven, P, Flick, D, Nicolaï, B, & Alvarez, G (2006). Modelling transport phenomena in refrigerated food bulks, packages and stacks: basics and advances. International Journal of Refrigeration, 29(6), 985–997.CrossRefGoogle Scholar
  41. Zadravec, P, Veberic, R, Stampar, F, Eler, K, & Schmitzer, V (2013). Fruit size prediction of four apple cultivars: accuracy and timing. Scientia Horticulturae, 160, 177–181.CrossRefGoogle Scholar
  42. Zihua, S (2011). Statistical shape modelling: automatic shape model building. PhD thesis, University College London.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Femke Danckaers
    • 1
  • Toon Huysmans
    • 1
  • Mattias Van Dael
    • 2
  • Pieter Verboven
    • 2
  • Bart Nicolaï
    • 2
  • Jan Sijbers
    • 1
  1. 1.imec - Vision Lab, Department of PhysicsUniversity of AntwerpAntwerpBelgium
  2. 2.Division of Mechatronics, Biostatistics and Sensors (MeBioS), Department of BiosystemsK.U. LeuvenHeverleeBelgium

Personalised recommendations