AdaBoost classifiers for pecan defect classification

Computers and Electronics in Agriculture

Volumn 77, Issue 1, 2011, Pages 60-68

  Mathanker, S K ^a   Weckler, P R ^a   Bowser, T J ^a   Wang, N ^a   Maness, N O ^a  

^a OKLAHOMA STATE UNIVERSITY   (United States)

Author keywords

AdaBoost; Food safety machine vision inspection; Machine learning; Pattern recognition; Pecan; Support vector machine

Indexed keywords

ADABOOST; CLASSIFICATION ACCURACY; CLASSIFICATION TIME; COMPUTATIONAL TIME; DATA VARIABILITY; DEFECT CLASSIFICATION; LINEAR SVM; MACHINE LEARNING; MACHINE VISION INSPECTION; ORDERS OF MAGNITUDE; OTSU METHOD; PECAN; REAL-TIME APPLICATION; SEGMENTATION METHODS; WATER FLOWS; X-RAY IMAGE;

COMPUTER VISION; FLOW OF WATER; FOOD PRODUCTS; HEALTH; HYDRAULICS; SUPPORT VECTOR MACHINES;

ADAPTIVE BOOSTING;

ACCURACY ASSESSMENT; FOOD PRODUCTION; FOOD SAFETY; VECTOR;

CARYA ILLINOINENSIS;

EID: 79957744656     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2011.03.008     Document Type: Article

References (25)

1. Barnes M., Duckett T., Cielniak G., Stround G., Harper G. Visual detection of blemishes in potatoes using minimalist boosted classifiers. Journal of Food Engineering 2010, 98(3):339-346.
2. Cortes C., Vapnik V. Support-vector networks. Machine Learning 1995, 20(3):273-297.
3. Food and Agriculture Organization (FAO), 1995. Non-Wood Forest Products. Food and Agriculture Organization of the United Nations, Rome, Italy. Available at: Accessed 17 February, 2011. http://www.fao.org/docrep/v8929e/v8929e00.htm.
4. Freund, Y., Schapire, R.E., 1996. Experiments with a new boosting algorithm. In: Proceedings of the Thirteenth International Conference on Machine Learning, pp. 148-156.
5. Freund Y., Schapire R.E. Improved boosting algorithms using confidence-rated predictions. Machine Learning 1999, 37(3):297-336.
6. Freund Y., Schapire R.E. A short introduction to boosting. Journal of Japanese Society for Artificial Intelligence 1999, 14(5):771-780.
7. Friedman J., Hastie T., Tibshirani R. Additive logistic regression: A statistical view of boosting. The Annals of Statistics 2000, 28(2):337-374.
8. García-Pedrajas N. Supervised projection approach for boosting classifiers. Pattern Recognition 2009, 42(9):1742-1760.
9. Gonzalez, R.C., Woods, R.E., 2008.Digital Image Processing, third ed., Pearson Education Inc., Upper Saddle River, NJ.
10. Haff R.P., Toyofuku N. X-ray detection of defects and contaminants in food industry. Sensing and Instrumentation for Food Quality and Safety 2008, 2:262-273.
11. Hur, A.B., Ong, C.S., Sonnenburg, S., Scholkopf, B., Rätsch, G., 2008. Support Vector Machines and Kernels for Computational Biology. PLoS Computational Biology. Available at: Accessed on Accessed on 30 April, 2010. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2547983/pdf/pcbi.1000173.pdf.
12. Karimi Y., Prasher S.O., Patel R.M., Kim S.H. Application of support vector machine technology for weed and nitrogen stress detection in corn. Computers and Electronics in Agriculture 2006, 51(1-2):99-109.
13. Kotwaliwale N., Weckler P.R., Brusewitz G.H., Kranzler G.A., Maness N.O. Non-destructive quality determination of pecans using soft X-rays. Postharvest Biology and Technology 2007, 45:372-380.
14. Mathanker, S.K., 2010. Development of a new local adaptive thresholding method and classification algorithms for X-ray machine vision inspection of pecans. Ph. D dissertation, Department of Biosystems andAgricultural Engineering, Oklahoma State University, Stillwater, OK.
15. Mathanker S.K., Weckler P.R., Wang N., Bowser T., Maness N.O. Local adaptive thresholding of pecan X-ray images: Reverse water flow method. Transactions of the ASABE 2010, 53(3):961-969.
16. Mathanker, S.K., Weckler, P.R., Taylor, R.K., Fan, G., 2010b. AdaBoost and support vector machine classifiers for automatic weed control: Canola and Wheat. ASABE Meeting Paper No. 1008834. St. Joseph, Mich.: ASABE.
17. Math Works Incl, 2007. Image Processing Toolbox User's Guide, for Use with MATLAB. The Math Works Inc., Natick, MA.
18. Meir R., Rätsch G. An introduction to boosting and leveraging: In: Advanced Lectures on Machine Learning 2003, Springer, Berlin, pp. 118-183.
19. Mertayak, C., 2007. AdaBoost: The meta machine learning algorithm formulated by Yoav Freund and Robert Schapire. Available at: Accessed on 1 February, 2010. http://www.mathworks.com/matlabcentral/fileexchange/21317-adaboost.
20. Oh H.H., Lim K.T., Chien S.I. An improved binarization algorithm based on a water flow model for document image with inhomogeneous backgrounds. Pattern Recognition 2005, 38:2612-2625.
21. Otsu N. A threshold selection method from gray level histograms. IEEE Transactions on Systems Man and Cybernetics Part C 1979, 9(1):62-66.
22. Rätsch G., Warmuth M.K. Efficient margin maximizing with boosting. Journal of Machine Learning Research 2005, 6(12):2131-2152.
23. Santerre C.R. Chapter 4: pecan processing. In: Pecan Technology 1994, Chapman and Hall, New York, NY, pp. 57-58.
24. Vezhnevets, A. 2006. GML AdaBoost MATLAB Toolbox. Available at: Accessed on 2 October, 2009. http://research.graphicon.ru.
25. Wang D., Wang M., Qiao X. Support vector machines regression and modeling of greenhouse environment. Computer and Electronic Agriculture 2009, 66(1):46-52.