Multi-phase cross-correlation method for motion estimation of fertiliser granules during centrifugal spreading

Multi-phase cross-correlation method for motion estimation of fertiliser granules during... Excessive fertiliser use has been a main contributor to the increasing environmental imbalance observed in the past 20 years. Better accuracy in spreading would limit excess fertiliser loss into the environment. Increased accuracy begins by understanding the fertiliser spreading process from the vane to the soil. Our work concentrates on the use of centrifugal spreaders, as these are most commonly used in Europe. Progress in imaging devices and image processing has resulted in the availability of new technologies to use when describing the behaviour of fertiliser granules during ejection from centrifugal spreaders. Fertiliser deposition on the soil can be predicted using a ballistic flight model, but this requires determination of the velocities and the directions of the granules when they leave the spinning disc. This paper presents improvements to the high speed imaging system that we had previously developed, i.e. enhancements to the illumination and the image processing. The illumination of the previous system, which used many separate flashes, did not give consistent illumination. We have improved it by using a stroboscope with power-LEDs, located at 1 m height around the digital camera and controlled by a Field-programmable gate array (FPGA) card. The image processing has been improved by development of a multi-phase method based on a cross-correlation algorithm. We have compared the cross-correlation method to the Markov Random Fields (MRF) method previously implemented. These tests, based on multi-exposure images, revealed that cross-correlation method gives more accurate results than the MRF technique, with guaranteed sub-pixel accuracy. Knowing that an error of one pixel can lead to a prediction error between 200 and 500 mm on the ground, the latter method gives an accuracy range between 0.1 and 0.4 pixels, whereas the MRFs technique is limited to 3 and 9 pixels for the vertical and horizontal components of the velocities, respectively. The sub-pixel accuracy of the new method was proven by applying it on simulated images with known displacements between the grains. By using a realistic spreading model, the simulated images are similar to those obtained with a high speed imaging system. This sub-pixel accuracy now makes it possible to decrease the resolution of the camera to that of a classical high-speed camera. These improvements have created an affordable and durable system appropriate for installation on a spreader. Farmers could use this system to both calibrate the spreader and verify the fertiliser distribution on the ground. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Multi-phase cross-correlation method for motion estimation of fertiliser granules during centrifugal spreading

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Publisher
Springer US
Copyright
Copyright © 2010 by Springer Science+Business Media, LLC
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1007/s11119-010-9193-2
Publisher site
See Article on Publisher Site

Abstract

Excessive fertiliser use has been a main contributor to the increasing environmental imbalance observed in the past 20 years. Better accuracy in spreading would limit excess fertiliser loss into the environment. Increased accuracy begins by understanding the fertiliser spreading process from the vane to the soil. Our work concentrates on the use of centrifugal spreaders, as these are most commonly used in Europe. Progress in imaging devices and image processing has resulted in the availability of new technologies to use when describing the behaviour of fertiliser granules during ejection from centrifugal spreaders. Fertiliser deposition on the soil can be predicted using a ballistic flight model, but this requires determination of the velocities and the directions of the granules when they leave the spinning disc. This paper presents improvements to the high speed imaging system that we had previously developed, i.e. enhancements to the illumination and the image processing. The illumination of the previous system, which used many separate flashes, did not give consistent illumination. We have improved it by using a stroboscope with power-LEDs, located at 1 m height around the digital camera and controlled by a Field-programmable gate array (FPGA) card. The image processing has been improved by development of a multi-phase method based on a cross-correlation algorithm. We have compared the cross-correlation method to the Markov Random Fields (MRF) method previously implemented. These tests, based on multi-exposure images, revealed that cross-correlation method gives more accurate results than the MRF technique, with guaranteed sub-pixel accuracy. Knowing that an error of one pixel can lead to a prediction error between 200 and 500 mm on the ground, the latter method gives an accuracy range between 0.1 and 0.4 pixels, whereas the MRFs technique is limited to 3 and 9 pixels for the vertical and horizontal components of the velocities, respectively. The sub-pixel accuracy of the new method was proven by applying it on simulated images with known displacements between the grains. By using a realistic spreading model, the simulated images are similar to those obtained with a high speed imaging system. This sub-pixel accuracy now makes it possible to decrease the resolution of the camera to that of a classical high-speed camera. These improvements have created an affordable and durable system appropriate for installation on a spreader. Farmers could use this system to both calibrate the spreader and verify the fertiliser distribution on the ground.

Journal

Precision AgricultureSpringer Journals

Published: Sep 19, 2010

References

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