Protocol for automating error removal from yield maps

Abstract

Yield mapping is one of the most widely used precision farming technologies. However, the value of the maps can be compromised by the presence of systematic and random errors in raw within field data. In this paper, an automated method to clean yield maps is proposed so as to ensure the quality of further data processing and management decisions. First, data were screened by filtering null and edge yield values as well global outliers. Second, spatial outliers or local defective observations were deleted. The local Moran’s index of spatial autocorrelation and Moran’s plot were used as tool to identify the spatial outliers. The protocol to filter out global and local outliers was evaluated on 595 real yield datasets from different grain crops. Significant improvements in the distribution and spatial structure of yield datasets was found. Approximately 30% of the dataset size was removed from each monitor dataset, with one third of the removal occurring during filtering of spatial outliers. The automation of null, edge yield values and the removal of global outliers improved yield distributions, whereas the cleaning of local outliers impacted the yield spatial structure for all yield maps and crops. The algorithm proposed to clean yield maps is easy to apply for preprocessing the growing number of available yield maps.