US 12,354,345 B2
Methods and systems for visualizing soybean variety placement using variety profile index
William Kess Berg, Clayton, IN (US); Jon J. Fridgen, Lancaster, KY (US); Jonathan Michael Bokmeyer, Zionsville, IN (US); and Andrew James Woodyard, Champaign, IL (US)
Assigned to ADVANCED AGRILYTICS HOLDINGS, LLC, Indianapolis, IN (US)
Filed by ADVANCED AGRILYTICS HOLDINGS, LLC, Indianapolis, IN (US)
Filed on Mar. 20, 2024, as Appl. No. 18/610,659.
Application 18/610,659 is a continuation of application No. 18/584,420, filed on Feb. 22, 2024.
Application 18/584,420 is a continuation of application No. 18/377,693, filed on Oct. 6, 2023.
Application 18/377,693 is a continuation of application No. 18/088,259, filed on Dec. 23, 2022, granted, now 11,783,578, issued on Oct. 10, 2023.
Application 18/377,693 is a continuation of application No. 17/987,758, filed on Nov. 15, 2022, granted, now 11,783,577, issued on Oct. 10, 2023.
Application 18/088,259 is a continuation of application No. 17/694,661, filed on Mar. 14, 2022, granted, now 11,574,466, issued on Feb. 7, 2023.
Application 17/987,758 is a continuation of application No. 17/694,661, filed on Mar. 14, 2022, granted, now 11,574,466, issued on Feb. 7, 2023.
Claims priority of provisional application 63/174,386, filed on Apr. 13, 2021.
Prior Publication US 2024/0221377 A1, Jul. 4, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06V 20/10 (2022.01); A01B 79/00 (2006.01); G06F 18/214 (2023.01); G06N 20/00 (2019.01)
CPC G06V 20/188 (2022.01) [A01B 79/005 (2013.01); G06F 18/2155 (2023.01); G06N 20/00 (2019.01)] 21 Claims
OG exemplary drawing
 
1. A computing system, comprising:
one or more processors; and
one or more non-transitory, computer-readable media having stored thereon instructions that, when executed by the one or more processors, cause the computing system to at least:
receive a machine data set corresponding to one or more agricultural fields, wherein the machine data set is labeled with known variety profile index values;
retrieve one or more spatial data files corresponding to each agricultural field;
process the spatial data files and the machine data set using a regression machine learning model to generate predicted variety profile index values corresponding to each of a plurality of sub-field environments within each agricultural field;
determine, based upon the predicted variety profile index values, a plurality of environment-specific varietal responses for each agricultural field;
generate (1) a multi-genetics planting recommendation corresponding to the environment-specific varietal responses, and (2) a map layer showing respective predicted variety profile index values for each agricultural field; and
display the multi-genetics planting recommendation and the map layer on a graphical user interface.