Machine learning optimization through randomized autonomous crop planting

A non-transitory computer-readable medium comprising memory with instructions encoded thereon, the instructions, when executed by one or more processors, causing the one or more processors to perform operations, the instructions comprising instructions to:

• plant each of a plurality of portions of a field, each portion of the field planted using an agricultural vehicle configured to randomly vary each of a plurality of planting parameters when planting the portion of the field, the planting parameters including at least a planting depth, a planting spacing, a type of treatment applied when planting, and an amount of treatment applied when planting;
• determine, for each portion of the field, a resulting crop outcome across the portion of the field;
• generate a training set of data comprising the varied planting parameters and the determined crop outcome for each of the portions of the field;
• train a machine-learned model using the generated training set of data, the machine-learned model configured to predict a crop outcome for a portion of the field based on a set of planting parameters used to plant the portion of the field;
• and
• for a subsequent iteration, for a target portion of the field, apply the machine-learned model to identify a set of planting parameters for planting the target portion of the field for a desired crop outcome.

An agricultural vehicle comprising one or more hardware processors and a non-transitory computer-readable storage medium storing executable instructions that, when executed by the one or more hardware processors, cause the agricultural vehicle to perform steps comprising:

• planting each of a plurality of portions of a field, each portion of the field planted using an agricultural vehicle configured to randomly vary each of a plurality of planting parameters when planting the portion of the field, the planting parameters including at least a planting depth, a planting spacing, a type of treatment applied when planting, and an amount of treatment applied when planting;
• determining, for each portion of the field, a resulting crop outcome across the portion of the field;
• generating a training set of data comprising the varied planting parameters and the determined crop outcome for each of the portions of the field;
• training a machine-learned model using the generated training set of data, the machine-learned model configured to predict a crop outcome for a portion of the field based on a set of planting parameters used to plant the portion of the field;
• and
• for a subsequent iteration, for a target portion of the field, applying the machine-learned model to identify a set of planting parameters for planting the target portion of the field for a desired crop outcome.