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In precision agriculture, the reduction of herbicide applications requires an accurate detection of weed patches.
From image detection, to quantify weed infestations, it would be necessary to identify crop rows from line detection
algorithm and to discriminate weed from crop. Our laboratory developed several methods for line detection
based on Hough Transform, double Hough Transform or Gabor filtering. The Hough Transform is well adapted
to image affected by perspective deformations but the computation burden is heavy and on-line applications are
hardly handled. To lighten this problem, we have used a Gabor filter to enhance the crop rows present into the
image but, if this method is robust with parallel crop rows (without perspective distortions), it implies to deform
image with an inverse projection matrix to be applied in the case of an embedded camera. We propose, in order to
implement a filter in the scale / space domain, to use a discrete dyadic wavelet transform. Thus, we can extract the
vertical details contained in various parts of the image from different levels of resolution. Each vertical detail level
kept allows to enhance the crop rows in a specific part of the initial image. The combination of these details enable
us to discriminate crop from weeds with a simple logical operation. This spatial method, thanks to the fast wavelet
transform algorithm, can be easily implemented for a real time application and it leads to better results than those
obtained from Gabor filtering. For this method, the weed infestation rate is estimated and the performance are
compared to those given by other methods. A discussion concludes about the ability of this method to detect the
crop rows in agronomic images. Finally we consider the ability of this spatial-only approach to classify weeds
from crop.
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