Using machine learning to identify a degradation state of an autonomous agricultural vehicle

A server comprising:

• one or more processors;
• and
• memory operatively coupled to the one or more processors, the memory comprising instructions that, when executed by the one or more processors, cause the one or more processors to:
• generate a training data set comprising historical instructions provided by historical autonomous agricultural vehicles to spray nozzles of the historical autonomous agricultural vehicles;
• train a machine-learned model using the training data set to identify an autonomous agricultural vehicle degradation state based on instructions provided by an autonomous agricultural vehicle to spray nozzles of the autonomous agricultural vehicle;
• receive, for a target autonomous agricultural vehicle, nozzle spray instructions generated by the target autonomous agricultural vehicle using image data captured by the target autonomous agricultural vehicle, the nozzle spray instructions comprising nozzle-level commands to spray nozzles indicating whether to spray at a given location for each of the spray nozzles;
• apply the machine-learned model to the received spray nozzle instructions provided by the target autonomous agricultural vehicle;
• and
• in response to identifying a degradation state for the target autonomous agricultural vehicle using the machine-learned model, modify a state of the target autonomous agricultural vehicle.

A non-transitory computer-readable recording medium storing computer-readable instructions that cause one or more processors to perform operations comprising:

• generating a training data set comprising historical instructions provided by historical autonomous agricultural vehicles to spray nozzles of the historical autonomous agricultural vehicles;
• training a machine-learned model using the training data set to identify an autonomous agricultural vehicle degradation state based on instructions provided by an autonomous agricultural vehicle to spray nozzles of the autonomous agricultural vehicle;
• receiving, for a target autonomous agricultural vehicle, nozzle spray instructions generated by the target autonomous agricultural vehicle using image data captured by the target autonomous agricultural vehicle, the nozzle spray instructions comprising nozzle-level commands to spray nozzles indicating whether to spray at a given location for each of the spray nozzles;
• applying the machine-learned model to the received spray nozzle instructions provided by the target autonomous agricultural vehicle;
• and
• in response to identifying a degradation state for the target autonomous agricultural vehicle using the machine-learned model, modifying a state of the target autonomous agricultural vehicle.