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DECONVOLUTION OF SITE-SPECIFIC YIELD MEASUREMENTS TO ADDRESS PEANUT COMBINE DYNAMICS

机译:特定于产量的花生组合动力学的去卷积反卷积

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During the development of a peanut yield monitoring system, experiments were conducted on a two-row peanut combine to determine the duration of time lag between pickup and yield measurement, and to characterize the convolution of peanut flow within the combine. The research indicates that the two-row peanut combine used in the experiment subjects harvested product to significant convolution. A simple time lag correction will not recover the site specific (short term accuracy) of yield measurements. The distance and time period required to achieve a yield estimate error less than 20% (95% confidence) is greater than 19.7 m (17 s) for simple time lag correction while it is 5.8 m (5 s) for deconvoluted data. The net result is that smaller regions of yield variability may be recognized with greater confidence using the deconvolution method than with the simple time delay method.
机译:在开发花生产量监控系统的过程中,对两行花生联合收割机进行了试验,以确定捡拾和产量测量之间的时间间隔,并表征了花生在联合收割机内的流动。研究表明,用于实验对象的两排花生联合收割机收获的产品卷积明显。简单的时滞校正将无法恢复产量测量的特定地点(短期精度)。对于简单的时间滞后校正,实现小于20%(95%置信度)的产量估算误差所需的距离和时间段大于19.7 m(17 s),而对去卷积数据则为5.8 m(5 s)。最终结果是,与简单的时间延迟方法相比,使用反卷积方法可以更可靠地识别较小的产量变异区域。

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