Copyright and Licensing
Articles accepted for publication will be licensed under the Creative Commons BY-NC-SA. Authors must sign a non-exclusive distribution agreement after article acceptance.
Boosting classifiers for weed seeds identification
Keywords:
machine vision, classification, boosting, neural networksAbstract
The identification and classification of seeds are of major technical and economical importance in the agricultural industry. To automate these activities, like in ocular inspection one should consider seed size, shape, color and texture, which can be obtained from seed images. In this work we complement previous studies on the discriminating power of these characteristics for the unique identification of seeds of 57 weed species. In particular, we discuss the possibility of improving the naïve Bayes and artificial neural network classifiers previously developed in order to avoid the use of color features as classification parameters. Morphological and textural seed characteristics can be obtained from black and white images, which are easier to process and require a cheaper hardware than color ones. To this end, we boost the classification methods by means of the AdaBoost.M1 technique, and compare the results obtained with the performance achieved when using color images. We conclude that boosting the naïve Bayes and neural classifiers does not fully compensate the discriminating power of color features. However, the improvement in classification accuracy might be enough to make the classifier still acceptable in practical applications.
Downloads
Download data is not yet available.
References
[1] Ahmad, I.S., Reid, J.F., Paulsen, M.R., Sinclair, J.B. “Color classifier for symptomatic soybean seeds using image processing”, Plant Disease 83, 1999, pp. 320-327.
[2] Bishop, C.M. Neural Networks for Pattern Recognition. Clarendon Press, Oxford, 1995.
[3] Breiman, L. “Bagging predictors”. Machine Learning 26, 1996, pp. 123-140.
[4] Chen, C., Chiang, Y.P., Pomeranz, Y. “Image analysis and characterization of cereal grains with a laser range finder and camera contour extractor”. Cereal Chem. 66(6), 1989, pp. 466-470.
[5] Chtioui, Y., Bertrand, D., Dattée, Y., Devaux, M.F. “Identification of seeds by color imaging: Comparison of discriminant analysis and artificial neural networks”. J. Sci. Food Agric. 71, 1996, pp. 433-441.
[6] Chtioui, Y., Bertrand, D., Barba, D. “Feature selection by a genetic algorithm. Application to seed discrimination by artificial vision”. J. Sci. Food Agric. 76, 1998, pp. 77-86.
[7] Dietterich, T.G. “Proper statistical tests for comparing supervised classification learning algorithms”. Technical Report. Department of Computer Science, Oregon State University, Corvallis, OR, 1996.
[8] Draper, S.R., Travis, A.J. “Preliminary observations with a computer based system for analysis of the shape of seeds and vegetative structures”. Journal of National Institute of Agricultural Botany 16, 1984. pp. 387-395.
[9] Freund Y., Schapire, R.E. “A decision theoretic generalization of on-line learning and an application to boosting”. Journal of Computer and System Sciences 55(1) , 1997, pp. 119-139.
[10] Galloway, M.M. “Textural analysis using gray level run length”. Computer Graphics and Image Processing 4, 1975, pp. 172-179.
[11] Granitto, P.M., Verdes, P.F., Navone, H.D., Ceccatto, H.A. “Automatic Identification of Weed Seeds by Color Image Processing”. VI Argentine Congress on Computer Science (Ushuaia, Argentina), 2000.
[12] Granitto, P.M., Navone, H.D., Verdes, P.F., Ceccatto, H.A. “Weed Seeds Identification by Machine Vision”. Computers and Electronics in Agriculture 33, 2002, pp. 91-103.
[13] Haralick, R.M., Shanmugam, K., Dinstein, I. “Textural features for image classification.”. IEEE Transactions on Systems, Man, and Cybernetics 3(6), 1973, pp. 610-621.
[14] Jain, A., Zongker, D. “Feature selection: Evaluation, application, and small sample performance”. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(2) , 1997, pp. 153-158.
[15] Jansen, P.I. “Seed production quality in Trifolium balansae and T. resupinatum: The role of seed color”. Seed Sci. Technol. 23, 1995, pp. 353-364.
[16] Keefe, P.D., Draper, S.R., “The measurement of new characters for cultivar identification in wheat using machine vision”. Seed Sci. Technol. 14, 1986, pp. 715-724.
[17] Neuman, M.R., Sapirstein, H.D., Shwedyk, E., Bushuk, W. “Discrimination of wheat class and variety by digital image analysis of whole grain samples”. J. Cereal Sci. 6, 1987, pp. 125-132.
[18] Neuman, M.R., Sapirstein, H.D., Shwedyk, E., Bushuk, W. “Wheat grain color analysis by digital image processing I. Methodology”. J. Cereal Sci. 10, 1989, pp. 175-182.
[19] Neuman, M.R., Sapirstein, H.D., Shwedyk, E., Bushuk, W. “Wheat grain color analysis by digital image processing II. Wheat class discrimination”. J. Cereal Sci. 10, 1989, pp. 183-188.
[20] Petersen, P.E.H., Krutz, G.W. “Automatic identification of weed seeds by color machine vision”. Seed Sci. Technol. 20, 1992, pp. 193-208.
[21] Sapirstein, H.D., Neuman, M., Wright, E.H., Shwedyk, E., Bushuk, W. “An instrumental system for cereal grain classification using digital image analysis”. J. Cereal Sci. 6, 1987, pp. 3-14.
[22] Schapire, R.E. “The strength of weak learnability”. Machine Learning 5(2) , 1990, pp. 197-227.
[23] Symons, S.J., Fulcher, R.G. “Determination of wheat kernel morphological variation by digital image analysis: I. Variation in Eastern Canadian Milling Quality”. Wheats. J. Cereal Sci. 8, 1988, pp. 211-218.
[24] Zayas, I., Pomeranz, Y., Lai, F.S. “Discrimination of wheat and nonwheat components in grain samples by image analysis”. Cereal Chem. 66(6) , 1989, pp. 233-237.
[2] Bishop, C.M. Neural Networks for Pattern Recognition. Clarendon Press, Oxford, 1995.
[3] Breiman, L. “Bagging predictors”. Machine Learning 26, 1996, pp. 123-140.
[4] Chen, C., Chiang, Y.P., Pomeranz, Y. “Image analysis and characterization of cereal grains with a laser range finder and camera contour extractor”. Cereal Chem. 66(6), 1989, pp. 466-470.
[5] Chtioui, Y., Bertrand, D., Dattée, Y., Devaux, M.F. “Identification of seeds by color imaging: Comparison of discriminant analysis and artificial neural networks”. J. Sci. Food Agric. 71, 1996, pp. 433-441.
[6] Chtioui, Y., Bertrand, D., Barba, D. “Feature selection by a genetic algorithm. Application to seed discrimination by artificial vision”. J. Sci. Food Agric. 76, 1998, pp. 77-86.
[7] Dietterich, T.G. “Proper statistical tests for comparing supervised classification learning algorithms”. Technical Report. Department of Computer Science, Oregon State University, Corvallis, OR, 1996.
[8] Draper, S.R., Travis, A.J. “Preliminary observations with a computer based system for analysis of the shape of seeds and vegetative structures”. Journal of National Institute of Agricultural Botany 16, 1984. pp. 387-395.
[9] Freund Y., Schapire, R.E. “A decision theoretic generalization of on-line learning and an application to boosting”. Journal of Computer and System Sciences 55(1) , 1997, pp. 119-139.
[10] Galloway, M.M. “Textural analysis using gray level run length”. Computer Graphics and Image Processing 4, 1975, pp. 172-179.
[11] Granitto, P.M., Verdes, P.F., Navone, H.D., Ceccatto, H.A. “Automatic Identification of Weed Seeds by Color Image Processing”. VI Argentine Congress on Computer Science (Ushuaia, Argentina), 2000.
[12] Granitto, P.M., Navone, H.D., Verdes, P.F., Ceccatto, H.A. “Weed Seeds Identification by Machine Vision”. Computers and Electronics in Agriculture 33, 2002, pp. 91-103.
[13] Haralick, R.M., Shanmugam, K., Dinstein, I. “Textural features for image classification.”. IEEE Transactions on Systems, Man, and Cybernetics 3(6), 1973, pp. 610-621.
[14] Jain, A., Zongker, D. “Feature selection: Evaluation, application, and small sample performance”. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(2) , 1997, pp. 153-158.
[15] Jansen, P.I. “Seed production quality in Trifolium balansae and T. resupinatum: The role of seed color”. Seed Sci. Technol. 23, 1995, pp. 353-364.
[16] Keefe, P.D., Draper, S.R., “The measurement of new characters for cultivar identification in wheat using machine vision”. Seed Sci. Technol. 14, 1986, pp. 715-724.
[17] Neuman, M.R., Sapirstein, H.D., Shwedyk, E., Bushuk, W. “Discrimination of wheat class and variety by digital image analysis of whole grain samples”. J. Cereal Sci. 6, 1987, pp. 125-132.
[18] Neuman, M.R., Sapirstein, H.D., Shwedyk, E., Bushuk, W. “Wheat grain color analysis by digital image processing I. Methodology”. J. Cereal Sci. 10, 1989, pp. 175-182.
[19] Neuman, M.R., Sapirstein, H.D., Shwedyk, E., Bushuk, W. “Wheat grain color analysis by digital image processing II. Wheat class discrimination”. J. Cereal Sci. 10, 1989, pp. 183-188.
[20] Petersen, P.E.H., Krutz, G.W. “Automatic identification of weed seeds by color machine vision”. Seed Sci. Technol. 20, 1992, pp. 193-208.
[21] Sapirstein, H.D., Neuman, M., Wright, E.H., Shwedyk, E., Bushuk, W. “An instrumental system for cereal grain classification using digital image analysis”. J. Cereal Sci. 6, 1987, pp. 3-14.
[22] Schapire, R.E. “The strength of weak learnability”. Machine Learning 5(2) , 1990, pp. 197-227.
[23] Symons, S.J., Fulcher, R.G. “Determination of wheat kernel morphological variation by digital image analysis: I. Variation in Eastern Canadian Milling Quality”. Wheats. J. Cereal Sci. 8, 1988, pp. 211-218.
[24] Zayas, I., Pomeranz, Y., Lai, F.S. “Discrimination of wheat and nonwheat components in grain samples by image analysis”. Cereal Chem. 66(6) , 1989, pp. 233-237.
Downloads
Published
2003-04-01
Issue
Section
Original Articles
License
How to Cite
[1]
“Boosting classifiers for weed seeds identification”, JCS&T, vol. 3, no. 01, pp. p. 34–39, Apr. 2003, Accessed: Mar. 06, 2026. [Online]. Available: https://journal.info.unlp.edu.ar/JCST/article/view/950
