Clustering and Classification Algorithms in Food and Agricultural Applications: A Survey

  • Radnaabazar Chinchuluun
  • Won Suk Lee
  • Jevin Bhorania
  • Panos M. Pardalos
Part of the Springer Optimization and Its Applications book series (SOIA, volume 25)


Data mining has become an important tool for information analysis in many disciplines. Data clustering, also known as unsupervised classification, is a popular data-mining technique. Clustering is a very challenging task because of little or no prior knowledge. Literature review reveals researchers’ interest in development of efficient clustering algorithms and their application to a variety of real-life situations. This chapter presents fundamental concepts of widely used classification algorithms including k-means, k-nearest neighbor, artificial neural networks, and fuzzy c-means. We also discuss applications of these algorithms in food and agriculture sciences including fruits classification, machine vision, wine classification, and analysis of remotely sensed forest images.


Artificial Neural Network Synthetic Aperture Radar Machine Vision Citrus Fruit Probabilistic Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Abdullah, M.Z., Mohamad-Saleh, J., Fathinul-Syahir, A.S., Mohd-Azemi, B.M.N., 2006. Discrimination and classification of fresh-cut starfruits (Averrhoa carambola L.) using automated machine vision system. J. Food Eng. 76, 506–523.CrossRefGoogle Scholar
  2. 2.
    Anderson, K.A., Magnuson, B.A., Tschirgi, M.L., Smith, B., 1999. Determining the geographic origin of potatoes with trace metal analysis using statistical and neural network classifiers. J. Agric. Food Chem. 47(4), 1568–1575.CrossRefGoogle Scholar
  3. 3.
    Annamalai, P., Lee, W.S., Burks, T.F., 2004. Color vision system for estimating citrus yield in real-time. ASAE Paper No. 043054. St. Joseph, MI: ASAE.Google Scholar
  4. 4.
    Atkinson, P.M., Tatnall, A.R.L., 1997. Neural networks in remote sensing. Int. J. Remote Sens. 18(4), 699–709.CrossRefGoogle Scholar
  5. 5.
    Baraldi, A., Blonda, P., 1999. A survey of fuzzy clustering algorithms for pattern recognition—Part I. IEEE Trans. Sys. Man Cyber. – Part B, Cybernetics 29(6), 778–785.Google Scholar
  6. 6.
    Baraldi, A., Blonda, P., 1999. A survey of fuzzy clustering algorithms for pattern recognition—Part II. IEEE Trans. Sys. Man Cyber. – Part B, Cybernetics 29(6), 786–801.Google Scholar
  7. 7.
    Beltrán, N.H., Duarte-Mermoud, M.A., Bustos, M.A., Salah, S.A., Loyola, E.A., Peña-Neira, A.I., Jalocha, J.W., 2006. Feature extraction and classification of Chilean wines. J. Food Eng. 75, 1–10.CrossRefGoogle Scholar
  8. 8.
    Benati, S., 2006. Categorical data fuzzy clustering: an analysis of local search heuristics. Comput. Oper. Res. (in press).Google Scholar
  9. 9.
    Bermejo, S., Cabestany, J., 2000. Adaptive soft k-nearest-neighbour classifiers. Pattern Recognit. 33, 1999–2005.MATHCrossRefGoogle Scholar
  10. 10.
    Bezdek, J., 1981. Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum.MATHGoogle Scholar
  11. 11.
    Bishop, C.M., 1995. Neural Networks for Pattern Recognition. Oxford: Oxford University Press.Google Scholar
  12. 12.
    Boginski, V., Butenko, S., Pardalos, P.M., 2006. Mining market data: A network approach. Comput. Oper. Res. 33(11), 3171–3184.MATHCrossRefGoogle Scholar
  13. 13.
    Bradley, S., Fayyad, M., 1998. Refining initial points for k-means clustering. In: J. Shavlik (Ed.), Proceedings of the 15th International Conference on Machine Learning (ICML98). San Francisco: Morgan Kaufmann, pp. 91–99.Google Scholar
  14. 14.
    Busygin, S., Prokopyev, O.A., Pardalos, P.M., 2005. Feature selection for consistent biclustering via fractional 0.1 programming. Comb. Optim. 10, 7–21.MathSciNetMATHCrossRefGoogle Scholar
  15. 15.
    Castińeira Gómez, M.D.M., Fledmann, I., Jakubowski, N., Andersson, J.T., 2004. Classification of German white wines with certified brand of origin by multielement quantitation and pattern recognition techniques. J. Agric. Food Chem. 52, 2962–2974.CrossRefGoogle Scholar
  16. 16.
    Chinchuluun, R., Lee, W.S., 2006. Citrus yield mapping system in natural outdoor scenes using the Watershed transform. ASAE Paper No. 063010. St. Joseph, MI: ASAE.Google Scholar
  17. 17.
    Chung, K.L., Lin, J.S., 2007. Faster and more robust point symmetry-based k-means algorithm. Pattern. Recognit. 40(2), 410–422.MathSciNetMATHCrossRefGoogle Scholar
  18. 18.
    Diamantopoulou, M.J., 2005. Artificial neural networks as an alternative tool in pine bark volume estimation. Comput. Electron. Agric. 48, 235–244.CrossRefGoogle Scholar
  19. 19.
    Díaz, C., Conde, J.E., EstéVez, D., Olivero, S.J.P., Trujillo, J.P.P., 2003. Application of multivariate analysis and artificial neural networks for the differentiation of red wines from the canary islands according to the island of origin. J. Agric. Food Chem. 51, 4303–4307.CrossRefGoogle Scholar
  20. 20.
    Duda, R.O., Hart, P.E., 1973. Pattern Classification and Scene Analysis. New York: John Wiley & Sons.MATHGoogle Scholar
  21. 21.
    Duda, R.O., Hart, P.E., Stork, D.G., 2001. Pattern Classification (2nd edition). New York: Wiley.Google Scholar
  22. 22.
    Dunn, J., 1974. A fuzzy relative of the ISODATA process and its use in detecting compact well separated clusters. J. Cyber. 3(3), 32–57.MathSciNetCrossRefGoogle Scholar
  23. 23.
    Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., 1996. From data mining to knowledge discovery in databases. AI Magazine – AAAI 17(3), 37–54.Google Scholar
  24. 24.
    Fukunaga, K., Narendra, P.M., 1975. A branch and bound algorithm for computing k-nearest neighbors. IEEE Trans. Comput. 24(7), 750–753.MathSciNetMATHCrossRefGoogle Scholar
  25. 25.
    Granitto, P.M., Verdes, P.F., Ceccatto, H.A., 2005. Large-scale investigation of weed seed identification by machine vision. Comput. Electron. Agric. 47, 15–24.CrossRefGoogle Scholar
  26. 26.
    Guyer, D., Yang, X., 2000. Use of genetic artificial neural networks and spectral imaging for defect detection on cherries. Comput. Electron. Agric. 29, 179–194.CrossRefGoogle Scholar
  27. 27.
    Hammah, R.E., Curran, J.H., 2000. Validity measures for the fuzzy cluster analysis of orientations. IEEE Trans. Patt. Anal. Mach. Intel. 22(12), 1467–1472.CrossRefGoogle Scholar
  28. 28.
    Hansen, P., Mladenovis, N., 2001. J-means: a new local search heuristic for minimum sum of squares clustering. Pattern Recognit. 34, 405–413.MATHCrossRefGoogle Scholar
  29. 29.
    Hansen, L.K., Salamon, P., 1990. Neural network ensembles. IEEE Trans. Pattern Anal. Mach. Intell. 12(10), 993–1001.CrossRefGoogle Scholar
  30. 30.
    Hashem, S., Schmeiser, B., 1995. Improving model accuracy using optimal linear combinations of trained neural networks. IEEE Trans. Neural Netw. 6(3), 792–794.CrossRefGoogle Scholar
  31. 31.
    Hathaway, R., Bezdek, J., Hu, Y., 2000. Generalized fuzzy c-means clustering strategies using L norm distances. IEEE Trans. Fuzzy Syst. 8(5) 576–582.CrossRefGoogle Scholar
  32. 32.
    Hathaway, R., Bezdek, J., 2001. Fuzzy c-means clustering of incomplete data. IEEE Trans. Syst. Man Cyb. – Part B, Cybernetics 31(5) 735–744.Google Scholar
  33. 33.
    Holmstrom, H., Nilsson, M., Stahl, G., 2002. Forecasted reference sample plot data in estimations of stem volume using satellite spectral data and the k NN method. Int. J. Remote. Sens. 23(9), 1757–1774.CrossRefGoogle Scholar
  34. 34.
    Hoshi, T., Sasaki, T., Tsutsui, H., Watanabe, T., Tagawa, F., 2000. A daily harvest predict ion model of cherry tomatoes by mining from past averaging data and using topological case-based modeling. Comput. Electron. Agric. 29, 149–160.CrossRefGoogle Scholar
  35. 35.
    Hung, M., Yang, D., 2001. An efficient fuzzy c-means clustering algorithm. In: Proceedings IEEE International Conference on Data Mining, pp. 225–232.Google Scholar
  36. 36.
    Hwang, W.J., Wen, K.W., 1998. Fast k classification algorithm based on partial distance search. Electron. Lett. 34(21), 2062–2063.CrossRefGoogle Scholar
  37. 37.
    Jain, A.K., Dubes, R.C., 1988. Algorithms for Clustering Data. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
  38. 38.
    Jain, A.K., Murty, M.N., Flynn, P.J., 1999. Data clustering: a review. ACM Comput. Surv. 31(3), 264–323.CrossRefGoogle Scholar
  39. 39.
    James, M., 1985. Classification Algorithms. London: Collins Professional and Technical Books.MATHGoogle Scholar
  40. 40.
    Jayas, D.S., Paliwal, J., Visen, N.S., 2000. Multi-layer neural networks for image analysis of agricultural products. J. Agric. Eng. Res. 77(2), 119–128.CrossRefGoogle Scholar
  41. 41.
    Ji, C., Ma, S., 1997. Combinations of weak classifiers. IEEE Tran. Neural Netw. 8(1), 32–42.CrossRefGoogle Scholar
  42. 42.
    Jiang, X., Harvey, A., Wah, K.S., 2003. Constructing andtraining feed-forward neural networks for pattern classification. Pattern Recognit. 36, 853–867.CrossRefGoogle Scholar
  43. 43.
    Kanungo, T., Mount, D., Netanyahu, N., Piatko, C., Silverman, R., Wu, A., 2000. An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 881–892.CrossRefGoogle Scholar
  44. 44.
    Karayiannis, N.B., 1997. A methodology for constructing fuzzy algorithms for learning vector quantization. IEEE Trans. Neural Netw. 8(3), 505–518.CrossRefGoogle Scholar
  45. 45.
    Kiliç, K., Boyaci, Í.H., Köksel, H., Küsmenoglu, Í., 2007. A classification system for beans using computer vision system and artificial neural networks. J. Food Eng. 78(3), 897–904.Google Scholar
  46. 46.
    Kolen, J., Hutcheson, T., 2002. Reducing the time complexity of the fuzzy c-means algorithm, IEEE Trans. Fuzzy Syst. 10(2), 263–267.CrossRefGoogle Scholar
  47. 47.
    Kondo, N., Ahmad, U., Monta, M., Murase, H., 2000. Machine vision based quality evaluation of Iyokan orange fruit using neural networks, Comput. Electron. Agric. 29, 135–147.CrossRefGoogle Scholar
  48. 48.
    Krishna, K., Murty, M., 1999. Genetic k-means algorithm. IEEE Trans. Syst. Man Cyber. – Part B, Cybernetics 29(3), 433–439.Google Scholar
  49. 49.
    Kulkarni, S.R., Lugosi, G., Venkatesh, S.S., 1998. Learning pattern classification – a survey. IEEE Trans. Inf. Theory 44(6), 2178–2206.MathSciNetCrossRefGoogle Scholar
  50. 50.
    Kuncheva, L.I., 1997. Fitness functions in editing k-NN reference set by genetic algorithms. Pattern Recognit. 30(6), 1041–1049.CrossRefGoogle Scholar
  51. 51.
    Kuplich, T.M., 2006. Classifying regenerating forest stages in Amazônia using remotely sensed images and a neural network. Forest Ecol. Manage. 234(1–3), 1–9.CrossRefGoogle Scholar
  52. 52.
    Laszlo, M., Mukherjee, S., 2006. A genetic algorithm using hyper-quadtrees for low-dimensional k-means clustering. IEEE Trans. Pattern. Anal. Mach. Intell. 28(4) 533–543.CrossRefGoogle Scholar
  53. 53.
    Law, M.H.C.,Topchy, A.P., Jain, A.K., 2004. Multiobjective data clustering. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, 2, pp. 424–430.Google Scholar
  54. 54.
    Leemans, V., Destain, M.F., 2004. A real time grading method of apples based on features extracted from defects. J. Food Eng. 61, 83–89.CrossRefGoogle Scholar
  55. 55.
    Likasa, A., Vlassis, N., Verbeek, J.J., 2003. The global k-means clustering algorithm. Pattern Recognit. 36(2), 451–461.CrossRefGoogle Scholar
  56. 56.
    Linskens, H.F., Jackson, J.F., 1993. Wine analysis. In: Modern Methods of Plant Analysis. New Series, Volume 6.Google Scholar
  57. 57.
    MacQueen, J.B., 1967. Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability. Berkeley: University of California Press, Vol 1, pp. 281–297.Google Scholar
  58. 58.
    Majumdar, S., Jayas, D.S., 1999. Classification of bulk samples of cereal grains using machine vision. J. Agric. Eng. Res. 73(1), 35–47.MATHCrossRefGoogle Scholar
  59. 59.
    Marchant, J.A., Onyango, C.M., 2003. Comparison of a Bayesian classifier with a multilayer feed-forward neural network using the example of plant/weed/soil discrimination. Comput. Electron. Agric. 39, 3–22.CrossRefGoogle Scholar
  60. 60.
    Marique, T., Kharoubi, A., Bauffe, P., Ducattillion, C., 2003. Modeling of fried potato chips color classification using image analysis and artificial neural network. J. Food Eng. Phys. Prop. 68(7), 2263–2266.Google Scholar
  61. 61.
    Maulik, U., Bandyopadhyay, S., 2000. Genetic algorithm-based clustering technique. Pattern Recognit. 33, 1455–1465.CrossRefGoogle Scholar
  62. 62.
    Molto, E., Pla, F., Juste, F., 1992. Vision systems for the location of citrus fruit in a tree canopy. J. Agric. Eng. Res. 52, 101–110.CrossRefGoogle Scholar
  63. 63.
    Morimoto, T., Takeuchi, T., Miyata, H., Hashimoto, Y., 2000. Pattern recognition of fruit shape based on the concept of chaos and neural networks. Comput. Electron. Agric. 26, 171–186.CrossRefGoogle Scholar
  64. 64.
    Nakano, K., 1997. Application of neural networks to the color grading of apples. Comput. Electron. Agric. 18, 105–116.CrossRefGoogle Scholar
  65. 65.
    Nascimento, S., Mirkin, B., Moura-Pires, F., 2000. A fuzzy clustering model of data and fuzzy c-Means. The Ninth IEEE International Conference on Fuzzy Systems, Vol. 1, pp. 302–307.CrossRefGoogle Scholar
  66. 66.
    Pal, N.R., Bezdek, J.C., 1995. On cluster validity for the fuzzy c-means model. IEEE Trans. Fuzzy. Syst. 3(3), 370–379.CrossRefGoogle Scholar
  67. 67.
    Paliwal, J., Visen, N.S., Jayas, D.S., 2001. Evaluation of neural network architectures for cereal grain classification using morphological features. J. Agric. Eng. Res. 79(4), 361–370.CrossRefGoogle Scholar
  68. 68.
    Pan, J.S., Qiao, Y.L., Sun, S.H., 2004. A fast k nearest neighbors classification algorithm. IEICE Trans. Fund. Electron. Commun. Comput. E87-A(4), 961–963.Google Scholar
  69. 69.
    Papajorgji, P.J., Pardalos, P.M., 2005. Software Engineering Techniques Applied to Agricultural Systems. New York: Springer.Google Scholar
  70. 70.
    Parrish Jr., A.E., Goksel, A.K., 1977. Pictorial pattern recognition applied to fruit harvesting. Trans. ASAE 20(5), 822–827.Google Scholar
  71. 71.
    Pernkopf, F., 2005. Bayesian network classifiers versus selective k-NN classifier. Pattern Recognit. 38(1), 1–10.MATHCrossRefGoogle Scholar
  72. 72.
    Plebe, A., Grasso, G., 2001. Localization of spherical fruits for robotic harvesting. Mach. Vision. Appl. 13, 70–79.CrossRefGoogle Scholar
  73. 73.
    Pydipati, R., Burks, T.F., Lee, W.S., 2005. Statistical and neural network classfiers for citrus disease detection using machine vision. Trans. ASAE 48(5), 2007–2014.Google Scholar
  74. 74.
    Pydipati, R., Burks, T.F., Lee, W.S., 2006. Identification of citrus disease using color texture features and discriminant analysis. Comput. Electron. Agric. 52, 49–59.CrossRefGoogle Scholar
  75. 75.
    Qiao, Y.L.,Pan, J.S., Sun, S.H., 2004. Improved k nearest neighbor classification algorithm. The 2004 IEEE Asia-Pacific Conference on Circuits and Systems, 6–9 Dec., Vol. 2, pp. 1101–1104.CrossRefGoogle Scholar
  76. 76.
    Raptis, C.G., Siettos, C.I., Kiranoudis, C.T., Bafas, G.V., 2000. Classification of aged wine distillates using fuzzy and neural network systems. J. Food Eng. 46, 267–275.CrossRefGoogle Scholar
  77. 77.
    Reese, H., Nilsson, M., Sandström, P., Olsson, H., 2002. Applications using estimates of forest parameters derived from satellite and forest inventory data. Comput. Electron. Agric. 37, 37–55.CrossRefGoogle Scholar
  78. 78.
    Regunathan, M., Lee, W.S., 2005. Citrus yield mapping and size determination using machine vision and ultrasonic sensors. ASAE Paper No. 053017. St. Joseph, MI: ASAE.Google Scholar
  79. 79.
    Robinson, C., Mort, N., 1997. A neural network system for the protection of citrus crops from frost damage. Comput. Electron. Agric. 16, 177–187.CrossRefGoogle Scholar
  80. 80.
    Shahin, M.A., Tollner, E.W., McClendon, R.W., 2001. Artificial intelligence classifiers for sorting apples based on water core. J. Agric. Eng. Res. 79(3), 265–274.CrossRefGoogle Scholar
  81. 81.
    Simonton, W., Pease, J., 1993. Orientation independent machine vision classification of plant parts. J. Agric. Eng. Res. 54(3), 231–243.CrossRefGoogle Scholar
  82. 82.
    Slaughter, D.C., Harrell, R.C., 1987. Color vision in robotic fruit harvesting. Trans. ASAE 30(4), 1144–1148.Google Scholar
  83. 83.
    Slaughter, D.C., Harrell, R.C., 1989. Discriminating fruit for robotic harvest using color in natural outdoor scenes. Trans. ASAE 32(2), 757–763.Google Scholar
  84. 84.
    Solazzia, M., Uncinib, A., 2004. Regularising neural networks using flexible multivariate activation function. Neural. Netw. 17(2), 247–260.CrossRefGoogle Scholar
  85. 85.
    Su, M., Chou, C., 2001. A modified version of the K-means algorithm with a distance based on cluster symmetry. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 674–680.CrossRefGoogle Scholar
  86. 86.
    Sun, L.X., Danzer, K., Thiel, G., 1997. Classification of wine samples by means of artificial neural networks and discrimination analytical methods. Fres. J. Anal. Chem. 359, 143–149.Google Scholar
  87. 87.
    Uno, Y., Prasher, S.O., Lacroix, R., Goel, P.K., Karimi, Y., Viau, A., Patel, R.M., 2005. Artificial neural networks to predict corn yield from compact airborne spectrographic imager data. Comput. Electron. Agric. 47, 149–161.CrossRefGoogle Scholar
  88. 88.
    Wu, Y., Ianakiev, K., Govindaraju, V., 2002. Improved k-nearest neighbor classification. Pattern Recognit. 35, 2311–2318.MATHCrossRefGoogle Scholar
  89. 89.
    Xu, R., 2005. Survey of clustering algorithms. IEEE Trans. Neural Netw. 16(3), 645–678.CrossRefGoogle Scholar
  90. 90.
    Yager, R.R., Filev, D.P., 1994. Approximate clustering via the mountain method. IEEE Trans. Syst. Man Cyber. 24(8), 1279–1284.CrossRefGoogle Scholar
  91. 91.
    Yager, R.R., 2006. An extension of the naive Bayesian classifier. Inf. Sci. 176(5), 577–588.MathSciNetCrossRefGoogle Scholar
  92. 92.
    Yu, X., Chen, G., Cheng, S., 1995. Dynamic learning rate optimization of the back propagation algorithm. IEEE Trans. Neural Netw. 6(3), 669–677.CrossRefGoogle Scholar
  93. 93.
    Zhang, G.P., 2000. Neural networks for classification: a survey. IEEE Trans. Syst. Man Cyber. 30(4), 451–462.CrossRefGoogle Scholar
  94. 94.
    Zhang, J.S., Leung, Y.W., 2004. Improved possibilistic c-means clustering algorithms. IEEE Trans. Fuzzy. Syst. 12(2), 209–217.MathSciNetCrossRefGoogle Scholar
  95. 95.
    Zhang, Y., Xiong, Z., Mao, J., Ou, L., 2006. The study of parallel k-means algorithm. In: Proceedings of the 6th World Congress on Intelligent Control and Automation, Vol. 2, pp. 5868–5871.Google Scholar
  96. 96.
    Zhong, S., 2005. Efficient online spherical K-means clustering. In: Proceedings of International Joint Conference on Neural Networks, Vol. 5, pp. 3180–3185.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Radnaabazar Chinchuluun
  • Won Suk Lee
  • Jevin Bhorania
  • Panos M. Pardalos
    • 1
  1. 1.Department of Agricultural and Biological EngineeringUniversity of FloridaGainesvilleUSA

Personalised recommendations