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

被引：50

作者：

Bossu, J. ^{[1
]}

Gee, Ch. ^{[1
]}

Jones, G.

Truchetet, F. ^{[2
]}

机构：

[1] UP GAP, DSI, ENESAD, Quetigny, France

[2] CNRS, UMR uB 5158, Le Creusot, France

来源：

COMPUTERS AND ELECTRONICS IN AGRICULTURE | 2009年 / 65卷 / 01期

关键词：

Image processing; Wavelet transform; Gabor filtering; weed infestation rate; Synthetic images; IDENTIFICATION;

D O I：

10.1016/j.compag.2008.08.004

中图分类号：

S [农业科学];

学科分类号：

09 ;

摘要：

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. (C) 2008 Elsevier B.V. All rights reserved.

引用

页码：133 / 143

页数：11

共 36 条

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