论文标题
RITAS算法：建设性产量监控数据处理算法
The RITAS algorithm: a constructive yield monitor data processing algorithm
论文作者
论文摘要
已知收益率显示数据集包含不可靠记录的高比例。当前的工具集主要限于基于启发式或经验动机的统计规则的观察清洁程序，以实现极端价值识别和去除。我们提出了一种建设性算法，用于处理有据可查的产量监控数据工件，而无需诉诸数据删除。四步矩形的创建，交点分配和镶嵌，分配和平滑算法模型样本观测值作为重叠，不平等的，不等的，不规则的，时间序列的，分析的，分析的空间空间单元，以更好地复制破坏性采样的性质的性质。位置数据用于创建矩形的面积空间单位。定期的相交区域镶嵌和收获的质量分配会产生定期形状的和大小的多边形，将整个收获区域分配。最后，使用高斯过程平滑用于为地图用户提供空间趋势可视化。在玉米和大豆谷物产量图中说明了中间步骤以及算法输出，该图在美国鱼类和野生动物服务局尼尔史密斯国家野生动物保护区收集了五年的产量监测器数据。
Yield monitor datasets are known to contain a high percentage of unreliable records. The current tool set is mostly limited to observation cleaning procedures based on heuristic or empirically-motivated statistical rules for extreme value identification and removal. We propose a constructive algorithm for handling well-documented yield monitor data artifacts without resorting to data deletion. The four-step Rectangle creation, Intersection assignment and Tessellation, Apportioning, and Smoothing (RITAS) algorithm models sample observations as overlapping, unequally-shaped, irregularly-sized, time-ordered, areal spatial units to better replicate the nature of the destructive sampling process. Positional data is used to create rectangular areal spatial units. Time-ordered intersecting area tessellation and harvested mass apportioning generate regularly-shaped and -sized polygons partitioning the entire harvested area. Finally, smoothing via a Gaussian process is used to provide map users with spatial-trend visualization. The intermediate steps as well as the algorithm output are illustrated in maize and soybean grain yield maps for five years of yield monitor data collected at a research agricultural site located in the US Fish and Wildlife Service Neal Smith National Wildlife Refuge.
