Mass flow harvest detection tool for improved farming operations

A computerized system, comprising:

• at least one computer processor;
• and
• computer memory storing computer-useable instructions that, when used by at least one computer processor, cause the at least one computer processor to perform operations comprising:
• accessing, from a first sensor of a harvester, first sensor data comprising a first time-stamped signal indicative of a first parameter associated with crop harvesting performed within a field;
• accessing, from a second sensor of the harvester, second sensor data comprising a second time-stamped signal indicative of a second parameter associated with the crop harvesting performed within the field;
• cross-correlating the first time-stamped signal and the second time-stamped signal;
• based on cross-correlating the first time-stamped signal and the second time-stamped signal, determining a time shift between the first sensor data and the second sensor data;
• applying the time shift to the first sensor data to automatically generate a time-invariant data set;
• and
• based at least on the second sensor data and the time-invariant data set, generating a graphical user interface (GUI) comprising a plot indicative of at least one of the second sensor data or the time-invariant data set plotted against satellite positioning system data.

At least one computer-storage media having computer-executable instructions embodied thereon that, when executed by a computing system having a processor and memory, cause the processor to perform operations comprising:

• accessing first sensor data from a first sensor of a harvester and second sensor data from a second sensor of the harvester;
• extracting from the first sensor data a first machine learning (ML) feature indicative of a first parameter associated with crop harvesting performed within a field and from the second sensor data a second ML feature indicative of a second parameter associated with the crop harvesting performed within the field;
• based on the first ML feature and the second ML feature, determining, via a time-delay ML model, a time delay between the first sensor data and the second sensor data;
• applying a time shift equal to the time delay to the first sensor data to automatically generate a time-invariant data set;
• and
• based at least on the second sensor data and the time-invariant data set, causing a prescription map indicative of a plan for managing the field to be generated.