US 11,719,858 B2
Determination of location-specific weather information for agronomic decision support
Mirwaes Wahabzada, Langenfeld (DE); Holger Hoffmann, Langenfeld (DE); Eva Hill, Langenfeld (DE); Ole Peters, Langenfeld (DE); Christian Kerkhoff, Langenfeld (DE); Umit Baran Ilbasi, Langenfeld (DE); and Priyamvada Shankar, Langenfeld (DE)
Assigned to BASF Agro Trademarks GmbH, Ludwigshafen am Rhein (DE)
Appl. No. 17/55,432
Filed by BASF Agro Trademarks GmbH, Ludwigshafen am Rein (DE)
PCT Filed May 14, 2019, PCT No. PCT/EP2019/062315
§ 371(c)(1), (2) Date Nov. 13, 2020,
PCT Pub. No. WO2019/219664, PCT Pub. Date Nov. 21, 2019.
Claims priority of application No. 18172567 (EP), filed on May 16, 2018.
Prior Publication US 2021/0223433 A1, Jul. 22, 2021
Int. Cl. G01W 1/10 (2006.01); G06F 30/27 (2020.01); G01W 1/06 (2006.01); G06N 20/00 (2019.01); H04W 4/021 (2018.01)
CPC G01W 1/10 (2013.01) [G01W 1/06 (2013.01); G06F 30/27 (2020.01); G01W 2201/00 (2013.01); G01W 2203/00 (2013.01); G06N 20/00 (2019.01); H04W 4/021 (2013.01); Y02A 90/10 (2018.01)] 17 Claims
OG exemplary drawing
 
1. A method performed by at least one apparatus, said method comprising:
obtaining weather model data indicative of location-specific weather information for a first set of locations (26) on a first grid (28);
obtaining an area of interest (30) associated to at least one user (32);
obtaining and/or determining a second set of locations (34) based on a second grid (36) within said area of interest (30);
obtaining measurement data on location-specific weather information of a measurement device associated to said at least one user located at a measurement location (38) within and/or proximate to said area of interest (30);
determining, based at least partially on at least one machine learning process and on at least said obtained weather model data and said obtained measurement data, location-specific weather information for said second set of locations (34) based on said second grid (36);
obtaining and/or determining a third set of locations (40) based on a third grid (42) within said area of interest (30);
obtaining, based on at least one of a crop growth model and/or a crop growth measurement, surface topography data for at least one location within and/or proximate to said area of interest (30); and
determining, based at least partially on the at least one machine learning process and on said obtained surface topography data and said determined location-specific weather information for said second set of locations (34), location-specific weather information for said third set of locations (40) based on said third grid (42).