The RITAS algorithm: a constructive yield monitor data processing algorithm

TL;DR

The RITAS algorithm offers a constructive, non-destructive method for processing yield monitor data, improving reliability by modeling spatial units and smoothing data for better yield mapping.

Contribution

It introduces a novel four-step algorithm that models yield data as overlapping spatial units without data deletion, enhancing artifact handling in yield monitor datasets.

Findings

Applied to maize and soybean yield data over five years

Produced more reliable yield maps with improved spatial trend visualization

Demonstrated effectiveness in handling data artifacts without deletion

Abstract

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.