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

Jérémie Bossu 1 Christelle Gée 1 Gawain Jones 1 Frederic Truchetet 2
1 GAP - Unité Propre de Recherche - Génie des Agroéquipements et des Procédés
2 Le2i - Laboratoire Electronique, Informatique et Image [UMR6303]
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.
Keywords : Image processing Wavelet transform Gabor filtering Weed infestation rate Synthetic images
Document type :
Journal articles
Complete list of metadatas
https://hal-agrosup-dijon.archives-ouvertes.fr/hal-01826412
Contributor : Administrateur Agrosupdijon <>
Submitted on : Friday, June 29, 2018 - 1:48:05 PM
Last modification on : Thursday, December 13, 2018 - 4:12:07 PM

Identifiers

Collections

AGROSUP-DIJON | ENESAD | CNRS | LE2I | UNIV-BOURGOGNE | ICB

Citation

Jérémie Bossu, Christelle Gée, Gawain Jones, Frederic Truchetet. Wavelet transform to discriminate between crop and weed in perspective agronomic images. Computers and Electronics in Agriculture, Elsevier, 2009, 65 (1), pp.133 - 143. ⟨10.1016/j.compag.2008.08.004⟩. ⟨hal-01826412⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

91

Contact

CCSD