Imaging systems are progressing in both accuracy and robustness, and their use in precision agriculture is increasing accordingly. One application of imaging systems is to understand and control the centrifugal fertilizing spreading process. Predicting the spreading pattern on the ground relies on an estimation of the trajectories and velocities of ejected granules. The algorithms proposed to date have shown low accuracy, with an error rate of a few pixels. But a more accurate estimation of the motion of the granules can be achieved. Our new two-step cross-correlation-based algorithm is based on the technique used in particle image velocimetry (PIV), which has yielded highly accurate results in the field of fluid mechanics. In order to characterize and evaluate our new algorithm, we develop a simulator for fertilizer granule images that obtained a high correlation with the real fertilizer images. The results of our tests show a deviation of <0.2 pixels for 90% of estimated velocities. This subpixel accuracy allows for use of a smaller camera sensor, which decreases the acquisition and processing time and also lowers the cost. These advantages make it more feasible to install this system on existing centrifugal spreaders for real-time control and adjustment.
In the context of fertilizer supply reduction, the understanding of the whole centrifugal spreading process became
essential. Since few years we focused our research on the determination by image processing of the ejection conditions
of flight of the granules, that is the trajectories and ejection angles, used as input data for ballistic flight to predict the
fertilizer repartition on the ground. Due to relative high speed of the fertilizer granules (around 40 m.s-1), the previous
parameters were evaluated using a specific high speed imaging system and image processing based on motion estimation
method using Markov Random Fields method (MRFs). Even if the results were good (90% of correct trajectories), this
method needs an invariance of luminance between two successive images and a good initialization of the motion, quite
difficult to reach with the previous imaging device. In this paper we describe some improvements of the image
acquisition system (illumination management) and we tested image processings using Gabor filters and Block Matching
technique. The results obtained on synthesis images are satisfying but the specific fertilizer motion and behaviour needs
other improvements of these two previous methods to give accurate results in terms of speed and direction of the
granules. A comparison with the MRFs method is also currently investigating to propose a final reliable image
processing technique to be adapted for 3D estimation.
The management of mineral fertilization using centrifugal spreaders calls for the development of spread pattern characterization devices to improve the quality of fertilizer spreading. In order to predict spread pattern deposition using a ballistic flight model, several parameters need to be determined, in particular, the velocity of the granules when they leave the spinning disc. We demonstrate that a motion-blurred image acquired in the vicinity of the disc by a low-cost imaging system can provide the three-dimensional components of the outlet velocity of the particles. A binary image is first obtained using a recursive linear filter. Then an original method based on the Hough transform is developed to identify the particle trajectories and to measure their horizontal outlet angles, not only in the case of horizontal motion but also in the case of three-dimensional motion. The method combines a geometric approach and mechanical knowledge derived from spreading analysis. The outlet velocities are deduced from outlet angle measurements using kinematic relationships. Experimental results provide preliminary validations of the technique.
For activities of agronomical research institute, the land experimentations are essential and provide relevant information
on crops such as disease rate, yield components, weed rate... Generally accurate, they are manually done and present
numerous drawbacks, such as penibility, notably for wheat ear counting. In this case, the use of color and/or texture
image processing to estimate the number of ears per square metre can be an improvement. Then, different image
segmentation techniques based on feature extraction have been tested using textural information with first and higher
order statistical methods. The Run Length method gives the best results closed to manual countings with an average
error of 3%. Nevertheless, a fine justification of hypothesis made on the values of the classification and description
parameters is necessary, especially for the number of classes and the size of analysis windows, through the estimation of
a cluster validity index. The first results show that the mean number of classes in wheat image is of 11, which proves
that our choice of 3 is not well adapted. To complete these results, we are currently analysing each of the class
previously extracted to gather together all the classes characterizing the ears.
The management of mineral fertilisation using centrifugal spreaders requires the development of spread pattern
characterisation devices to improve the quality of fertiliser spreading. In order to predict the spread pattern deposition
using a ballistic flight model, several parameters need to be determined and especially the velocity of the granules when
they leave the spinning disc. This paper demonstrates that a motion blurred image acquired in the vicinity of the disc
with a low cost imaging system can provide the three dimensional components of the outlet velocity of the particles. A
binary image is first obtained using a recursive linear filter. Then an original method based on the Hough transform is
developed to identify the particle trajectories and to measure their horizontal outlet angles, not only in the case of
horizontal motion but also in the case of three dimensional motion. The method combines a geometric approach and
mechanical knowledge derived from spreading analysis. The outlet velocities are deduced from the outlet angle
measurements using kinematic relationships.
In agronomic domain, the simplification of crop counting, necessary for yield prediction and agronomic studies, is an important project for technical institutes such as Arvalis. Although the main objective of our global project is to conceive a mobile robot for natural image acquisition directly in a field, Arvalis has proposed us first to detect by image processing the number of wheat ears in images before to count them, which will allow to obtain the first component of the yield. In this paper we compare different texture image segmentation techniques based on feature extraction by first and higher order statistical methods which have been applied on our images. The extracted features are used for unsupervised pixel classification to obtain the different classes in the image. So, the K-means algorithm is implemented before the choice of a threshold to highlight the ears. Three methods have been tested in this feasibility study with very average error of 6%. Although the evaluation of the quality of the detection is visually done, automatic evaluation algorithms are currently implementing. Moreover, other statistical methods of higher order will be implemented in the future jointly with methods based on spatio-frequential transforms and specific filtering.
In Europe, centrifugal spreading is a widely used method for agricultural soil fertilization. In this broadcasting method, fertilizer particles fall onto a spinning disk, are accelerated by a vane, and afterward are ejected into the field. To predict and control the spread pattern, a low-cost, embeddable technology adapted to farm implements must be developed. We focus on obtaining the velocity and the direction of fertilizer granules when they begin their flight by means of a simple imaging system. We first show that the outlet angle of the vane is a bounded value and that its measurement provides the outlet velocity of the particle. Consequently, a simple camera unit is used in the vicinity of the spinning disk to acquire digital images on which trajectory streaks are recorded. Information is extracted using the Hough transform, which is specifically optimized to analyze these streaks and to measure the motion of the particles. The optimization takes into account prior mechanical knowledge and tackles the problem of Hough space quantization. The method is assessed on various simulated images and is used on real spreading images to characterize fertilizer particle trajectories.
Although mechanically simple, centrifugal spreaders used for mineral fertilization involve complex physics that cannot be fully characterized at the present time. To avoid fertilizer misadjustments in the field, centrifugal spreading, and especially the initial conditions of flight of the granules, have to be accurately understood. The work described in this paper led to the conception of a high speed images collection system for characterizing the centrifugal spreading in a laboratory. This patented multiexposure system allows to determine granule trajectories after their ejection, with the use of a high resolution low cost digital camera, combined with a set of flashes, and different motion estimation methods. The Markov Random Fields (MRFs) method gives very accurate and better results in comparison with intercorrelation or theoretical modeling of the granule throws methods. This establishment allows to use the results in ballistic model to predict the fertilizer repartition on the ground. A fourth motion estimation method based on Gabor filters is moreover currently investigated.
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