谷物联合收割机在线产量监测综述——测产传感方法、产量图重建和动力学模型

为深入了解测产方法、产量图重建和动力学模型的研究内容及关键技术,对测产方法、产量图重建、谷物流的动力学模型以及产量测量中的误差等研究成果进行梳理。重点概述了测产方法的分类,介绍了不同测产方法的原理、产量图重建涉及到的关键技术和动力学模型上取得的成果;对测产方法的试验结果和优缺点进行比较;分析了测产方法、产量图重建、水分传感器、切割宽度传感器和GPS定位装置等研究的误差来源。结果表明:1)对不同方式的测产装置进行合理的安装、校准和操作,就能使测产结果达到足够的精度,建议对不同的测产方式加强误差分析并提高试验准确度。2)产量图重建过程中的部分误差通过校准可以减小甚至消除,但基于小面积地块的产量图构建及误差研究还有待加强。3)一阶动力学模型无法确定谷物混合对产量监测的影响,建议在基于非线性组合算法和反褶积算法的动力学模型上加强研究。

Research review on online grain yield monitoring for combine harvester: Yield sensing, yield mapping and dynamic model

In order to deeply understand the research contents and key technologies of yield sensing, yield mapping and dynamic model, the research results of yield sensing, yield mapping, dynamic model of grain flow and errors in yield sensing were sorted out. This review mainly summarized the classification of yield measurement methods. The principles of different yield sensing, the key technologies involved in yield mapping and the results obtained from the dynamic model were introduced. The test results and the advantages and disadvantages of the yield sensing were compared. The error sources of yield sensing, yield mapping, moisture sensors, cutting width sensors and GPS positioning device were analyzed. The results show that: 1)Reasonable installation, calibration and operation of production measuring devices in different ways can make the production measurement results achieve enough accuracy. It is suggested to strengthen error analysis of different production measuring methods and improve test accuracy. 2)Some errors in the yield mapping can be reduced or even eliminated by calibration, but the production plot construction and error research based on small plots need to be strengthened. 3)The first-order dynamic model cannot determine the impact of grain mixing on yield monitoring. Therefore, research on the dynamic model based on nonlinear combination algorithm and deconvolution algorithm should be strengthened.