Abstract
The measure of leaf damage is a basic tool in plant epidemiology research. Measuring the area of a great number of leaves is subjective and time consuming. We investigate the use of machine learning approaches for the objective segmentation and quantification of leaf area damaged by mites in avocado leaves. After extraction of the leaf veins, pixels are labeled with a look-up table generated using a Support Vector Machine with a polynomial kernel of degree 3, on the chrominance components of YCrCb color space. Spatial information is included in the segmentation process by rating the degree of membership to a certain class and the homogeneity of the classified region. Results are presented on real images with different degrees of damage.
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Kerguelen, V., Hoddle, M.S.: Measuring mite feeding damage on avocado leaves with automated image analysis software. Florida Entomologist 82, 119–122 (1999)
Wijekoon, C., Goodwin, P., Hsiang, T.: Quantifying fungal infection of plant leaves by digital image analysis using scion image software. Journal of Microbiological Methods 74, 94–101 (2008)
Bakr, E.M.: A new software for measuring leaf area, and area damaged by tetranychus urticae koch. Journal of Applied Entomology 129, 173–175 (2005)
Otsu, N.: A tlreshold selection method from gray-level histograms. IEEE Transactions on Systems, Man And Cybernetics 9, 62–66 (1979)
Finlayson, G.D., Schiele, B., Crowley, J.L.: Comprehensive colour image normalization. In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, p. 475. Springer, Heidelberg (1998)
Tek, F., Dempster, A., Kale, I.: A colour normalization method for giemsa-stained blood cell images. In: IEEE 14th Signal Processing and Communications Applications (2006)
Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1, 67–82 (1997)
Platt, J.: Machines using sequential minimal optimization. In: Schoelkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods - Support Vector Learning. MIT Press, Cambridge (1998)
Soille, P.: Morphological image analysis applied to crop field mapping. Image and Vision computing, 1025–1032 (2000)
Fu, H., Chi, Z.: A two-stage approach for leaf vein extraction. In: IEEE International conference on neural networks and signal processing (2003)
Li, Y., Chi, Z., Feng, D.D.: Leaf vein extraction using independent component analysis. In: IEEE Conference on Systems, Man, and Cybernetics (2006)
Nam, Y., Hwang, E., Kim, D.: A similarity-based leaf image retrieval scheme: Joining shape and venation features. Computer Vision and Image Understanding 110, 245–259 (2008)
Boese, B.L., Clinton, P.J., Dennis, D., Golden, R.C., Kim, B.: Digital image analysis of zostera marina leaf injury. Aquatic Botany 88, 87–90 (2008)
Clarke, J., Barman, S., Remagnino, P., Bailey, K., Kirkup, D., Mayo, S., Wilkin, P.: Venation pattern analysis of leaf images. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Remagnino, P., Nefian, A., Meenakshisundaram, G., Pascucci, V., Zara, J., Molineros, J., Theisel, H., Malzbender, T. (eds.) ISVC 2006. LNCS, vol. 4292, pp. 427–436. Springer, Heidelberg (2006)
Li, Y., Zhu, Q., Cao, Y., Wang, C.: A leaf vein extraction method based on snakes technique. In: International Conference on Neural Networks and Brain (2005)
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Díaz, G., Romero, E., Boyero, J.R., Malpica, N. (2009). Recognition and Quantification of Area Damaged by Oligonychus Perseae in Avocado Leaves. In: Bayro-Corrochano, E., Eklundh, JO. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2009. Lecture Notes in Computer Science, vol 5856. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10268-4_80
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DOI: https://doi.org/10.1007/978-3-642-10268-4_80
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