Automated adjustment of crop development estimates

A method of generating a crop development estimate for a crop variety, the method comprising:

• acquiring, by a mechanized harvester, ground-based samples associated with at least one trait of the crop variety in a plurality of sample locations;
• acquiring, by an image capture device, first image data comprising images associated with the at least one trait of the crop variety in the plurality of sample locations;
• generating, by a computing device comprising one or more processors, based on the ground-based samples of the crop variety, a plurality of ground-based estimates associated with the at least one trait of the crop variety in the plurality of sample locations;
• generating, by the computing device, based on inputting the first image data into one or more machine learning models, a plurality of first raw estimates corresponding to the plurality of sample locations;
• determining, by the computing device, for each of the plurality of sample locations, differences between the plurality of first raw estimates and the plurality of ground-based estimates;
• determining, by the computing device, based on predictive analysis of the plurality of first raw estimates and the plurality of ground-based estimates, a correction factor associated with minimization of the differences between the plurality of first raw estimates and the plurality of ground-based estimates in each of the plurality of sample locations;
• acquiring, by the image capture device, second image data comprising images of the crop variety in a second location that is not included in the plurality of sample locations;
• generating, by the computing device, based on inputting the second image data into the one or more machine learning models, a second raw estimate;
• and
• generating, by the computing device, based on fitting the correction factor to the second raw estimate, a corrected estimate for the crop variety in the second location.

One or more non-transitory computer readable media comprising instructions that, when executed by at least one processor, cause a computing device to perform operations comprising:

• receiving ground-based samples associated with at least one trait of a crop variety in a plurality of sample locations that were acquired by a mechanized harvester;
• receiving first image data acquired by an image capture device, wherein the first image data comprises images associated with the at least one trait of the crop variety in the plurality of sample locations;
• generating, based on the ground-based samples associated with at the least one trait of the crop variety, a plurality of ground-based estimates associated with the at least one trait of the crop variety in the plurality of sample locations;
• generating, based on inputting the first image data into one or more machine learning models, a plurality of first raw estimates corresponding to the plurality of sample locations;
• determining, for each of the plurality of sample locations, differences between the plurality of first raw estimates and the plurality of ground-based estimates;
• determining, based on a predictive analysis of the plurality of first raw estimates and the plurality of ground-based estimates, a correction factor associated with minimization of differences between the plurality of first raw estimates and the plurality of ground-based estimates in each of the plurality of sample locations;
• receiving, from the image capture device, second image data comprising images of the crop variety in a second location that is not included in the plurality of sample locations;
• generating, based on inputting the second image data into the one or more machine learning models, a second raw estimate;
• and
• generating, based on fitting the correction factor to the second raw estimate, a corrected estimate for the crop variety in the second location.