BALZARINI MONICA GRACIELA
Artículos
Título:
A random forest-based algorithm for data-intensive spatial interpolation in crop yield mapping
Autor/es:
CÓRDOBA, M.; BALZARINI, M.
Revista:
COMPUTERS AND ELETRONICS IN AGRICULTURE
Editorial:
ELSEVIER SCI LTD
Referencias:
Lugar: Amsterdam; Año: 2021 vol. 184
Resumen:
igh-resolution yield maps are an essential tool in modern agriculture. Using spatial interpolation, spatially discrete sampled yield data from yield monitors can be transformed into continuous yield maps. However, spatial interpolation is usually performed using methods that can be computationally demanding or that lack credibility measurements. The objectives of this work were to improve and evaluate a spatial machine learning algorithm for yield mapping at a fine scale. The core method used for mapping is Quantile Regression Forest Spatial Interpolation (QRFI), in which covariates from the spatial neighborhood of the sampled yields are used to predict yields at unsampled sites. To assess the algorithm performance, more than one thousand yield monitor datasets from several plant species were processed with QRFI, and other geostatistical (ordinary kriging, KG) and non-geostatistical (spatial inverse distance interpolation, IDW) methods. We illustrated the application of QRFI for yield