Abstract
A 3D imaging technique using a high speed binocular stereovision system was developed in combination with corresponding image processing algorithms for accurate determination of the parameters of particles leaving the spinning disks of centrifugal fertilizer spreaders. Validation of the stereo-matching algorithm using a virtual 3D stereovision simulator indicated an error of less than 2 pixels for 90% of the particles. The setup was validated using the cylindrical spread pattern of an experimental spreader. A 2D correlation coefficient of 90% and a Relative Error of 27% was found between the experimental results and the (simulated) spread pattern obtained with the developed setup. In combination with a ballistic flight model, the developed image acquisition and processing algorithms can enable fast determination and evaluation of the spread pattern which can be used as a tool for spreader design and precise machine calibration.
Highlights
Over the past 60 years the use of mineral fertilizers has allowed farmers to drastically increase their crop yields
Because reference data for the positions and velocities of fertilizer particles could not be obtained in practice, the system was validated by measuring the cylindrical spread pattern
The cylindrical spread pattern is less influenced by uncertainties in the ballistic flight model and does not require a large hall to measure the distribution of fertilizer particles on the ground
Summary
Over the past 60 years the use of mineral fertilizers has allowed farmers to drastically increase their crop yields. In most cases the fertilizer particles are collected in standardized trays and weighed [8] Because this is a long and fastidious method, several alternative techniques have been developed to characterize the spreading process more efficiently and calibrate spreaders [2,4,9,10,11,12,13,14,15,16]. Those hybrid techniques first determine the fertilizer particles’ ejection parameters (direction, speed and in some cases the size) and secondly use a ballistic flight model to estimate their landing points in the field [17]. The system has potential for affordable spreader calibration at farm level and as online feedback sensor on modern fertilizer spreaders
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