A random forest-based algorithm for data-intensive spatial interpolation in crop yield mapping

High-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 nongeostatistical (spatial inverse distance interpolation, IDW) methods. We illustrated the application of QRFI for yield mapping using yield monitor datasets of different grain crops from the Argentine Pampas. Evaluation of the methods showed that all statistical metrics suggested better results for yield maps obtained by QRFI than by KG or IDW. Globally, prediction error of QRFI was 11.5%, which was on average at least 16% better than the corresponding results of the other spatial interpolation methods. The machine learning algorithm QRFI can be successfully applied to perform spatial interpolation of yields at the field scale and to assess the associated prediction uncertainty.

Automated summary

This study aimed to improve and evaluate a spatial machine learning algorithm for yield mapping at a fine scale, and results showed that the algorithm performed better than other interpolation methods, with a prediction error rate of 11.5% and at least 16% better results on average.