Wavelet transform to discriminate between crop and weed in perspective agronomic images

Authors:
J. Bossu;Ch. Gée;G. Jones;F. Truchetet
Affiliations:
ENESAD/DSI/UP-GAP, 21 Bld Olivier de Serres, 21800 Quétigny, France;ENESAD/DSI/UP-GAP, 21 Bld Olivier de Serres, 21800 Quétigny, France;-;UMR 5158 uB-CNRS, 12 rue de la Fonderie, 71200 Le Creusot, France
Venue:
Computers and Electronics in Agriculture
Year:
2009

Citing 6
Cited 6

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Abstract

We proposed testing and validating the accuracy of four image processing algorithms (wavelet transforms and Gabor filtering) for crop/weed discrimination in synthetic and real images. A large panel of wavelet bases (33) was tested and the two best wavelets and the worst one were selected for detailed study. Based on a confusion matrix the crop/weed classification results of wavelet transforms were compared to the results of Gabor filtering that was initially chosen to develop a machine vision system for a real-time precision sprayer. The accuracy of these algorithms was compared and showed that wavelets were well adapted for perspective images: the best results were with Daubechies 25 and discrete approximation Meyer wavelets. They provided better results than Gabor filtering not only for crop/weed classification but also in processing time.