Machine learning methods and systems for variety profile index crop characterization
Abstract:
A system includes one or more processors; and one or more non-transitory, computer-readable media including instructions that, when executed by the one or more processors, cause the computing system to: receive a machine data set; process the machine data set with a trained machine-learned model to generate predicted variety profile index values; and cause a visualization to be displayed. A computer-implemented method includes receiving a machine data set; processing the machine data set with a trained machine-learned model to generate predicted variety profile index values; and causing a visualization to be displayed. A non-transitory computer-readable medium includes computer-executable instructions that, when executed by one or more processors, cause a computer to: receive a machine data set; process the machine data set with a trained machine-learned model to generate predicted variety profile index values; and cause a visualization to be displayed.
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