Use of High-Resolution Image Data Outperforms Vegetation Indices in Prediction of Maize Yield: Supplementary Methods
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'This is the supplementary methods of 'Use of High-Resolution Image Data Outperforms Vegetation Indices in Prediction of Maize Yield' published in Crop Science · May 2017, DOI: 10.2135/cropsci2017.01.0007. It includes the raw data in R format and the R-code for the analysis' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Bayesian prediction","High-throughput phenotyping","Hyperspectral image analysis","Maize","Yield prediction"]} <|eot_id|>
 Here is the Description of a dataset. Description:'This is the supplementary methods of 'Use of High-Resolution Image Data Outperforms Vegetation Indices in Prediction of Maize Yield' published in Crop Science · May 2017, DOI: 10.2135/cropsci2017.01.0007. It includes the raw data in R format and the R-code for the analysis' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
This is the supplementary methods of 'Use of High-Resolution Image Data Outperforms Vegetation Indices in Prediction of Maize Yield' published in Crop Science · May 2017, DOI: 10.2135/cropsci2017.01.0007. It includes the raw data in R format and the R-code for the analysis
Bayesian prediction; High-throughput phenotyping; Hyperspectral image analysis; Maize; Yield prediction