In the context of sustainable agriculture, matching accurately herbicides and weeds is an important task. The site specific spraying requires a preliminary diagnostic depending on the plant species identification and localisation. In order to distinguish between weeds species or to discriminate between weeds and soil from their spectral properties, we investigate a spectral approach developing a catadioptric bi-spectral imaging system as a diagnostic tool. The aim of this project consists in the conception and feasibility of a vision system which captures a pair of images with a single camera by the use of two planar mirrors. Then fixing a filter on each mirror, two different spectral channels (e.g. Blue and Green) of the scene can be obtained. The optical modeling is explained to shot the same scene. A calibration based on the inverse pinhole model is required to be able to superpose the scene. The choice of interferential filters is discussed to extract agronomic information from the scene by the use of vegetation index.
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
In the context of precision agriculture, we have developed a machine vision system for a real time precision
sprayer. From a monochrome CCD camera located in front of the tractor, the discrimination between
crop and weeds is obtained with an image processing based on spatial information using a Gabor filter.
This method allows to detect the periodic signals from the non periodic one and it enables to enhance
the crop rows whereas weeds have patchy distribution. Thus, weed patches were clearly identified by a blob-coloring method. Finally, we use a pinhole model to transform the weed patch coordinates image in world coordinates in order to activate the right electro-pneumatic valve of the sprayer at the right moment.
This paper presents two spatial methods to discriminate between crop and weeds. The application is related to agronomic image with perspective crop rows. The first method uses a double Hough Transform permitting a detection of crop rows and a classification between crop and weeds. The second method is based on Gabor filtering, a band pass filter. The parameters of this filter are detected from a Fast Fourier Transform of the image. For each method, a weed infestation rate is obtained. The two methods are compared and a discussion concludes about the abilities of these methods to detect the crop rows in agronomic images. Finally, we discuss this method regarding the capability of the spatial approach for classifying weeds from crop.