Protocol for automating error removal from yield maps

VEGA, A.; CÓRDOBA, M.; CASTRO-FRANCO, M.; BALZARINI, M.

PRECISION AGRICULTURE

SPRINGER

Año: 2019 vol. 20 p. 1030 - 1030

1385-2256

ield 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 outlie