Two-step cross correlation–based algorithm for motion estimation applied to fertilizer granules motion during centrifugal spreading.

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.