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

  • Published:2021-05 Issue: Volume:184 Page:106094
  • ISSN:0168-1699
  • Container-title:Computers and Electronics in Agriculture
  • language:en
  • Short-container-title:Computers and Electronics in Agriculture

Author:

Mariano Córdoba,Mónica Balzarini

Funder

Córdoba Ministerio de Ciencia Y Tecnología de Córdoba

Ministerio de Ciencia Tecnología e Innovación Productiva

National University of Cordoba Secretariat of Science and Technology

Publisher

Elsevier BV

Subject

Horticulture,Computer Science Applications,Agronomy and Crop Science,Forestry

Reference34 articles.

1. Spatial modelling with Euclidean distance fields and machine learning;Behrens;Eur. J. Soil Sci.,2018

2. Computational routines for the automatic selection of the best parameters used by interpolation methods to create thematic maps;Betzek;Comput. Electron. Agric.,2019

3. Random Forests;Breiman;Mach. Learn.,2001

4. The Data-Intensive Farm Management Project: Changing Agronomic Research Through On-Farm Precision Experimentation;Bullock;Agron. J.,2019

5. Canty, A., Ripley, B., 2019. boot: Bootstrap R (S-Plus) Functions. R package version 1.3-24.
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