Weed segmentation using texture features extracted from wavelet sub-images

Weed detection is a complicated problem which needs several sources of information to be gathered for successful discrimination. In this paper wavelet texture features were examined to verify their potential in weed detection in a sugar beet crop. Successive steps in a discrimination algorithm were designed to determine the wavelet texture features for each image sub-division to be fed to an artificial neural network. Co-occurrence texture features were determined for each multi-resolution image produced by single-level wavelet transform. Image segmentation was based on the decision made by neural network to label each sub-division as weed or main crop. Optimisation of the algorithm was tried by investigating two manners of discrimination of weeds from the main crop. Principal Component Analysis was used to select 14 from the 52 extracted texture features. Results demonstrated that the wavelet texture features were able to effectively discriminate weeds among the crops even when there was significant amount of occlusion and leaves overlapping.

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