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Adaboost and Support Vector Machine Classifiers for Automatic Weed Control: Canola and Wheat

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  2010 Pittsburgh, Pennsylvania, June 20 - June 23, 2010  1008834.(doi:10.13031/2013.29734)
Authors:   Sunil K Mathanker, Paul R Weckler, Randal K Taylor, Guoliang Fan
Keywords:   Adaboost, automatic weed control, canola, wheat, machine vision, precision agriculture

Detection of plant species for automatic weed control is the most challenging task. Detection accuracies can be improved by better sensing equipment and by accurate classifiers. Adaboost and support vector machine are two state-of-the-art classifiers. However, there are few studies applying them for plant species discrimination. This study attempted to investigate adaboost algorithms and support vector machine kernels for automatic weed control of canola and wheat. Individual images (64x64 pixels) of crop or weed plants, extracted from field digital color images, were used to extract features for classification. For canola, Real Adaboost was 3.29% more accurate than the Bayesian classifier and for wheat 3.09%. For canola, the radial basis function kernel based support vector machine was 2.67% more accurate than the Bayesian classifier and for wheat 4.77%. Overall, the Real Adaboost algorithm performed best among the selected adaboost algorithms and radial basis kernel among the selected support vector machine kernels.

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