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We have developed an impact-based mini- grain yield sensor (6 cm 4 cm) to achieve both handiness and accuracy especially for small-scale combines. For the accuracy, signal processing of the sensor was improved such that: 1) the output of the sensor was synchronized to the rotation of the grain auger whose upper edge is equipped with a blade for releasing the grain, 2) actual impacts of the grain were distinguished especially at a low flow rate of grain, and 3) zero-point of the sensor was compensated for automatically in the signal processing. For the handiness of calibration, 1) a non-linear model was proposed to relate individual impulse to the individual grain weight released at single rotation of the blade, and 2) the parameters were optimized simply using pairs of the signal recordings and the grain weights obtained in situ upon the harvest. In the field experiment, the sensor was installed on a 4-row Japanese-style rice (jidatsu) combine, and a total of 24 runs of wide flow rates of grain (0.17 0.84 kg/s) were harvested in two days of one week apart. The runs were divided in sequence into two datasets and calibration and full cross-validation were carried out. Relative errors of calibration were 2.4% and 2.7%, whereas those of validation were 2.2% and 2.5%. The method of signal processing showed robustness to extreme variation in flow rate of grain suited for practical applications.