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An application of soft sets in weed identification

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

Citation:  Paper number  131620222,  2013 Kansas City, Missouri, July 21 - July 24, 2013. (doi: @2013
Authors:   Pan Li, Dongjian He, Yongliang Qiao, Chenghai Yang
Keywords:   feature extraction image processing weed identification soft sets fuzzy soft sets

Abstract. Soft set theory is originally proposed as a general mathematical tool for dealing with uncertainties present in most of our real life. This study applied soft sets to improve low accuracy of weed identification caused by similar features. Firstly, three types of plant leaf features including shape, texture and fractal dimension were extracted from the plant leaves after a series of image processing. Then the weed-classification matrix went through arithmetic operations on the relation-matrices constructed with eigenvalues and their weight factor coefficients, from which the label corresponding to its largest membership in every row was selected, finally the weed was distinguished according to the label. Also the soft set theory showed higher performance in terms of robustness and algorithm complexity comparing with the Bayesian classifier, support vector machine (SVM) and back-propagation (BP) neural network. The proposed method provides a useful tool for weed identification and selectively spraying herbicide.

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