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

https://doi.org/10.1016/j.compag.2021.106094

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Geostatistical Applications for Precision Agriculture

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Sampling in Precision Agriculture

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Processing of Yield Map Data

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Protocol for automating error removal from yield maps

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Parallel ordinary kriging interpolation incorporating automatic variogram fitting

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A tutorial guide to geostatistics: Computing and modelling variograms and kriging

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Integration of high resolution remotely sensed data and machine learning techniques for spatial prediction of soil properties and corn yield

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Computational routines for the automatic selection of the best parameters used by interpolation methods to create thematic maps

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FastMapping: Software to create field maps and identify management zones in precision agriculture

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A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors

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Importance of spatial predictor variable selection in machine learning applications – Moving from data reproduction to spatial prediction

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Application of machine learning methods to spatial interpolation of environmental variables

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A generic framework for spatial prediction of soil variables based on regression-kriging

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Comparison of various uncertainty modelling approaches based on geostatistics and machine learning algorithms

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Sampling design optimization for soil mapping with random forest

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Random Forests

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Interpolation type and data computation of crop yield maps is important for precision crop production

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Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling

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Precision agriculture for grain production systems

inferredcites →https://doi.org/10.1097/ss.0000000000000115openalex-snapshot

Prediction of Soil Properties at Farm Scale Using a Model-Based Soil Sampling Scheme and Random Forest

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Spatial modelling with Euclidean distance fields and machine learning

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Comparison of several spatial prediction methods for soil pH

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Geostatistics or machine learning for mapping soil attributes and agricultural practices

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ranger : A Fast Implementation of Random Forests for High Dimensional Data in C++ and R

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Yield Editor: Software for Removing Errors from Crop Yield Maps

inferredcites →https://doi.org/10.2134/agronj2019.03.0165openalex-snapshot

The Data‐Intensive Farm Management Project: Changing Agronomic Research Through On‐Farm Precision Experimentation

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Random Forest Spatial Interpolation

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Machine Learning in Agriculture: A Review

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Precision agriculture for grain production systems

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Comparison of nearest-neighbor-search strategies and implementations for efficient shape registration

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Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables

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