Detection and instant confirmation of treatment action

An apparatus comprising at least one processor and a sensor, wherein the processor is configured to perform a method of sensor input processing, comprising:

• capturing, using the sensor, sensor images of a vicinity of a target object in an agricultural environment of a time interval during which a treatment action is applied to the target object;
• processing the sensor images using one or more machine learning (ML) algorithms wherein at least one ML algorithm uses an ML model trained to detect a presence of the treatment action in the vicinity of the target object by (1) detecting the presence of the treatment action in the sensor images by comparing multiple consecutive sensor images, (2) having the one or more ML algorithms draw inferences about presence of the target object in the sensor images containing the treatment action, (3) associating a confidence number to each inference, (4) detecting the presence of the target object by subjecting objects within a percent range of confidence to a feedback from a human user, and (5) detecting the presence of objects above a percent confidence without any feedback from the human user;
• and
• providing, selectively based on a result of detecting the presence of the treatment action in the vicinity of the target object, an outcome of the processing for further processing.

A non-transitory computer-readable medium having code stored thereon;

• the code, upon execution by a processor of an agricultural platform comprising a sensor, causing the processor to implement a method;
• comprising:
• capturing, using the sensor, sensor images of a vicinity of a target object of a time interval during which a treatment action is applied to the target object;
• processing the sensor images using one or more machine learning (ML) algorithms wherein at least one ML algorithm uses an ML model trained to detect a presence of the treatment action in the vicinity of the target object by (1) detecting the presence of the treatment action in the sensor images by comparing multiple consecutive sensor images, (2) having the one or more ML algorithms draw inferences about presence of the target object in the sensor images containing the treatment action, (3) associating a confidence number to each inference, (3) detecting the presence of the target object by subjecting objects within a percent range of confidence to a feedback from a human user, and (5) detecting the presence of objects above a percent confidence without any feedback from the human user;
• and
• providing, selectively based on a result of detecting the presence of the treatment action in the vicinity of the target object, an outcome of the processing for further processing.