AgDataBox-Map-Clean: A Web-Based Module for Screening Yield Monitor Data and Improving Yield Maps

Yield maps (YMs) are essential precision agriculture tools for assessing spatial variability in crop yields within an agricultural area. However, their quality is often compromised by duplicate points, negative or null points, outliers, and inliers, requiring the cleaning of raw harvest monitor data. This study aimed to develop, implement, and evaluate AgDataBox-Map-Clean v4.0.2 (ADB-Clean v4.0.2), a computational web module for cleaning raw harvest monitor data and integrating it with the AgDataBox-Map (ADB-Map) application from AgDataBox v4.0.2 (ADB). ADB is a web platform that integrates data, software, procedures, and methodologies for digital agriculture, and it is the only free, all-inclusive platform worldwide. The module cleans raw yield monitor data by removing duplicate points, negative or null points, outliers, and inliers. In a case study, three cleaning software programs (web module ADB-Clean v4.0.2, Map Filter v2.0, and Yield Editor v2.0.7) were compared. The YMs constructed from data processed by the three software programs were visually similar, but their similarity to raw-data YMs (without cleaning) was much worse. On average across the evaluated datasets, the cleaning process reduced the standard deviation (SD) by 68%, the coefficient of variation (CV) by 70%, and the dataset size by 23%, while increasing the mean yield by 11%. The results demonstrate that, for the datasets evaluated in this study, automated error removal from yield monitor data is feasible and can support improved agricultural decision-making. Because its cleaning algorithms operate on georeferenced point data, the module is expected to apply to other agricultural datasets, although this capability was not evaluated in the present study.