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Ensemble BLUP, Machine Learning, and Deep Learning Models Predict Maize Yield Better Than Each Model Alone.

Authors: Kick, Daniel;

Ensemble BLUP, Machine Learning, and Deep Learning Models Predict Maize Yield Better Than Each Model Alone.

Abstract

Data and scripts exploring ensembling strategies using the models developed in Kick et al., 2023 (see also 1, 2). Download all files to a single directory then run setup.sh or manually unzip using tar. Filename Description setup.sh Simple script that unzips zipped directories ext_data Reduced data from Kick et al. 2023 ext_data_notebooks Contains python notebooks containing analysis and R markdown file containing visualization of results. Python and R data objects are written to allow results to be read in instead of re-generated. output Folder containing a placeholder file. This research used resources provided by the United States Department of Agriculture’s Agricultural Research Service (project number 5070-21000-041-000-D). The SCINet project of the USDA Agricultural Research Service (project number 0500-00093-001-00-D) was instrumental in the training of the models used in this work. In addition, we would like to acknowledge those presently and historically involved in generating data for the Genomes to Fields Initiative.

Keywords

Machine Learning, Phenotypic Prediction, Ensemble

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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