Supervised classification of bruised apples with respect to the time after bruising on the basis of hyperspectral imaging data

Postharvest Biology and Technology

Volumn 86, Issue , 2013, Pages 249-258

  Baranowski, Piotr ^a   Mazurek, Wojciech ^a   Pastuszka Woźniak, Joanna ^a  

^a INSTITUTE OF AGROPHYSICS   (Poland)

Author keywords

Apple bruising; Hyperspectral imaging; Supervised classification; Time after bruising

Indexed keywords

EID: 84881241808     PISSN: 09255214     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.postharvbio.2013.07.005     Document Type: Article

References (41)

1. Baranowski P., Mazurek W., Witkowska-Walczak B., Slawiński C. Detection of early apple bruise with the use of pulsed-phase thermography. Postharvest Biol. Technol. 2009, 53:91-100.
2. Baranowski P., Mazurek W., Wozniak J., Majewska U. Detection of early bruises in apples using hyperspectral data and thermal imaging. J. Food Eng. 2012, 110:345-355.
3. Bobelyn E., Serban A.-S., Nicu M., Lammertyn J., Nicolai B.M., Saeys W. Postharvest quality of apple predicted by NIR-spectroscopy: study of the effect of biological variability on spectra and model performance. Postharvest Biol. Technol. 2010, 55:133-143.
4. Brosnan T., Sun D.W. Improving quality inspection of food products by computer vision - a review. J. Food Eng. 2004, 61:3-16.
5. Chen Y.R., Kim M.S. Visible/NIR imaging spectroscopy for assessing quality and safety of agro-foods. Proceedings of the NIR Publications 2004, 67-75. NIR, Chichester. A.M.C. Davies, A. Garrido (Eds.).
6. Drogoudi P.D., Pantelidis G. Effects of position on canopy and harvest time on fruit physico-chemical and antioxidant properties in different apple cultivars. Sci. Hort. 2011, 129:752-760.
7. ElMasry G., Wang N., Vigneault C., Qiao J., ElSayed A. Early detection of apple bruises on different background colors using hyperspectral imaging. LWT 2008, 41:337-345.
8. ElMasry G., Wang N., Vigneault C. Detecting chilling injury in Red Delicious apple using hyperspectral imaging and neural networks. Postharvest Biol. Technol. 2009, 52:1-8.
9. Ferguson I., Volz R., Woolf A. Preharvest factors affecting physiological disorders of fruit. Postharvest Biol. Technol. 1999, 15:255-262.
10. Grimm E., Khanal B.P., Winkler A., Knoche M., Köpcke D. Structural and physiological changes associated with the skin spot disorder in apple. Postharvest Biol. Technol. 2012, 64:111-118.
11. Hall M.A. Correlation-based Feature Subset Selection for Machine Learning 1998, University of Waikato, Hamilton, New Zealand, [PhD thesis].
12. Huang M., Lu R. Apple mealiness detection using hyperspectral scattering technique. Postharvest Biol. Technol. 2010, 58:168-175.
13. Kavdir I., Guyer D.E. Comparison of artificial neural networks and statistical classifiers in apple sorting using textural features. Biosyst. Eng. 2004, 89:331-344.
14. Kim M.S., Chen Y.R., Mehl P.M. Hyperspectral reflectance and fluorescence imaging system for food quality and safety. Trans. ASABE 2001, 44:721-729.
15. Kleynen O., Leemans V., Destain M.F. Selection of the most efficient wavelength bands for 'Jonagold' apple sorting. Postharvest Biol. Technol. 2003, 30:221-232.
16. Lee K., Kang S., Delwiche S.R., Kim M.S., Noh S. Correlation analysis of hyperspectral imagery for multispectral wavelength selection for detection of defects on apples. Sens. Instrumen. Food Qual. 2008, 2:90-96.
17. Leemans V., Magein H., Destain M.F. On-line fruit grading according to their external quality using machine vision. Biosyst. Eng. 2002, 83:397-404.
18. Lleó L., Roger J.M., Herrero-Langreo A., Diezma-Iglesias B., Barreiro P. Comparison of multispectral indexes extracted from hyperspectral images for the assessment of fruit ripening. J. Food Eng. 2011, 104:612-620.
19. Lu R., Chen Y.R., Park B. Hyperspectral imaging for detecting bruises in apples. ASAE Annual International Meeting 1999.
20. Lu R. Detection of bruises on apples using near-infrared hyperspectral imaging. Trans. ASAE 2003, 46:523-530.
21. Lu R. Multispectral imaging for predicting firmness and soluble solids content of apple fruit. Postharvest Biol. Technol. 2004, 31:147-157.
22. Lu R., Peng Y. Hyperspectral scattering for assessing peach fruit firmness. Biosyst. Eng. 2006, 93:161-171.
23. Lu R. Nondestructive measurements of firmness and soluble solids content for apple fruit using hyperspectral scattering images. Sensing Instr. Food Qual. Safety 2007, 1:19-27.
24. Mehl P.M., Chao K., Kim M.S., Chen Y.R. Detection of defects on selected apple cultivars using hyperspectral and multispectral image analysis. Appl. Eng. Agric. 2002, 18:219-226.
25. Mehl P.M., Chen Y.R., Kim M.S., Chan D.E. Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations. J. Food Eng. 2004, 61:67-81.
26. Mendoza F., Lu R., Ariana D., Cen H., Bailey B.B. Integrated spectral and image analysis of hyperspectral scattering data for prediction of apple fruit firmness and soluble solids content. Postharvest Biol. Technol. 2011, 62:149-160.
27. Nicolai B., Beullens K., Bobelyn E., Peirs A., Saeys W., Theron K.I., Lammertyn J. Nondestructive measurements of fruit and vegetable quality by means of NIR spectroscopy: a review. Postharvest Biol. Technol. 2007, 46:99-118.
28. Peng Y., Lu R. An LCTF-based multispectral imaging system for estimation of apple fruit firmness. Part II. Selection of optimal wavelengths and development of prediction models. Trans. ASAE 2006, 49:269-275.
29. Peng Y., Lu R. Analysis of spatially resolved hyperspectral scattering images for assessing apple fruit firmness and soluble solids content. Postharvest Biol. Technol. 2008, 48:52-62.
30. Qin J., Lu R., Peng Y. Prediction of apple internal quality using spectral absorption and scattering properties. Trans. ASABE 2009, 52:499-507.
31. Rasband W.S. ImageJ 1997-2011, U.S. National Institutes of Health, Bethesda, MD, USA, http://imagej.nih.gov/ij/.
32. Schatzki T.F., Haff R.P., Young R., Can I., Le L.C., Toyofuku N. Defect detection in apples by means of X-ray imaging. Trans. ASAE 1997, 40:1407-1415.
33. Shahin M.A., Tollner E.W., McClendon R.W., Arabnia H.R. Apple classification based on surface bruises using image processing and neural networks. Trans. ASAE 2002, 45:1619-1627.
34. Sun D.W. Computer Vision Technology for Food Quality Evaluation 2008, Academic Press/Elsevier, San Diego, CA, USA. 1st ed.
35. Sun D.W. Hyperspectral Imaging for Food Quality Analysis and Control 2010, Academic Press/Elsevier, San Diego, CA, USA. 1st ed.
36. Upchurch B.L., Throop J.A., Aneshansley D.J. Influence of time, bruise-type, and severity on near-infrared reflectance from apple surfaces for automatic bruise detection. Trans. ASAE 1994, 37:1571-1575.
37. Wang J., Nakano K., Ohashi S., Kubota Y., Takizawa K., Sasaki Y. Detection of external insect infestations in jujube fruit using hyperspectral reflectance imaging. Biosyst. Eng. 2011, 108:345-351.
38. Wen Z., Tao Y. Dual-camera NIR/MIR imaging for stem-end/calyx identification in apple defect sorting. Trans. ASAE 2000, 43:449-452.
39. Witten I.H., Frank E. Data Mining. Practical Machine Learning Tools and Techniques 2005, Morgan Kaufmann Publishers/Elsevier, San Francisco, CA, USA. 2nd ed.
40. Xing J., Bravo C., Jancso P., Ramon H., De Baerdemaeker J. Bruise detection on Golden Delicious apples by using hyperspectral imaging with multiple wavebands. Biosyst. Eng. 2005, 90:27-36.
41. Xing J., Jancso P., De Baerdemaeker J. Stem-end/calyx identification on apples using contour analysis in multispectral images. Biosyst. Eng. 2007, 96:231-237.