Velocity measurement using motion blurred images to improve the quality of fertiliser spreading in agriculture

S. Villette; F. Cointault; P. Zwaenepoel; B. Chopinet; M. Paindavoine

doi:10.1117/12.736744

29 May 2007 Velocity measurement using motion blurred images to improve the quality of fertiliser spreading in agriculture

S. Villette, F. Cointault, P. Zwaenepoel, B. Chopinet, M. Paindavoine

Author Affiliations +

Proceedings Volume 6356, Eighth International Conference on Quality Control by Artificial Vision; 63560I (2007) https://doi.org/10.1117/12.736744
Event: Eighth International Conference on Quality Control by Artificial Vision, 2007, Le Creusot, France

Abstract

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.

Citation Download Citation

S. Villette, F. Cointault, P. Zwaenepoel, B. Chopinet, and M. Paindavoine "Velocity measurement using motion blurred images to improve the quality of fertiliser spreading in agriculture", Proc. SPIE 6356, Eighth International Conference on Quality Control by Artificial Vision, 63560I (29 May 2007); https://doi.org/10.1117/12.736744

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