Improving the accuracy of estimating grain weight by discriminating each grain impact on the yield sensor

Improving the accuracy of estimating grain weight by discriminating each grain impact on the... This study was aimed at accurately estimating total weight of harvested grain on a combine by simply attaching a small yield sensor in the grain tank and by processing the output of the sensor. The yield sensor was first installed in a grain tank of a 1.2 m-swath Japanese-style (head-feeding or jidatsu) combine, and the weight was estimated from individual impulses received at each rotation of a grain-releasing device i.e. an auger blade. A non-linear relation was assumed between the weight of grain released and the impulse received, and the parameters of the non-linear model were optimized to minimize the sum of squares between the estimated and actual weight of grain accumulated at each run of the combine. A threshold for the output discriminated between actual release and no release of the grain from the auger blade. The appropriate range of the threshold was 4–6 times the root-mean squared output of the sensor without throughput (F rms ) of grain. The aim was to enhance the accuracy of the estimation of grain weight by disregarding signals that did not relate to the accumulation of grain in the tank. Two methods of calculating the impulses were proposed after the discrimination: “successive addition” and “interval addition”, and two non-linear models of converting impulses into the weight of grain: “odd function model” and “positive function model”. The use of the odd function model with the impulse calculated by the interval addition was the most robust, and root-mean squared relative errors of calibration and validation were both stable and around 2.5 % at a threshold of 5F rms . In the confirmatory experiment with a larger 1.8 m-swath Japanese-style grain combine equipped with the same sensor, the odd function model with the interval addition achieved root-mean squared relative error of 3.6 % at calibration and 4.4 % at validation at a threshold of 5F rms . Precision Agriculture Springer Journals

Improving the accuracy of estimating grain weight by discriminating each grain impact on the yield sensor

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Springer US
Copyright © 2013 by Springer Science+Business Media New York
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
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  • Grain yield mapping: Yield sensing, yield reconstruction, and errors
    Arslan, S; Colvin, T
  • A review of combine sensors for precision farming
    Reyns, P; Missotten, B; Ramon, H; Baerdemaeker, J

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