MACHINE LEARNING OPTIMIZATION THROUGH RANDOMIZED AUTONOMOUS CROP PLANTING

Abstract: systems and methods automate the design and execution of randomized experiments. portions of a field are planted using an agricultural vehicle configured to randomly vary planting parameters when planting a portion of the field. a resulting crop outcome across each portion or sub-portion of the field is observed. a training set of data is generated that includes the varied planting parameters and the associated crop outcomes for each portion of the field. a machine-learned model is trained using the training set of data and is configured to predict a crop outcome for a portion of the field based on historical and forecast conditions and a set of planting parameters applied to a portion of the field. for subsequent iterations, for a target portion of the field, the machine-learned model can be applied to identify a set of planting parameters for planting the target portion of the field to optimize a desired crop outcome.

Inventor(s): Michael D. Stellpflug, Angela L. Bowman

CPC Classification: A01B69/008 (Steering of agricultural machines or implements; Guiding agricultural machines or implements on a desired track)

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