US 12,004,444 B2
Predictive agricultural management system and method
Sarah Anne Placella, Orinda, CA (US); Adam Ralph Zeilinger, Berkeley, CA (US); Tyler Evan Schartel, Springfield, IL (US); and Ken Yamaguchi, Emeryville, CA (US)
Assigned to Root Applied Sciences Inc., Berkeley, CA (US)
Filed by Root Applied Sciences Inc., Berkeley, CA (US)
Filed on Apr. 24, 2023, as Appl. No. 18/138,584.
Application 18/138,584 is a continuation of application No. 16/941,217, filed on Jul. 28, 2020, granted, now 11,665,992.
Claims priority of provisional application 62/880,224, filed on Jul. 30, 2019.
Prior Publication US 2023/0255134 A1, Aug. 17, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. A01B 79/00 (2006.01); A01B 79/02 (2006.01); G06N 20/00 (2019.01); G06Q 50/02 (2012.01)
CPC A01B 79/005 (2013.01) [A01B 79/02 (2013.01); G06N 20/00 (2019.01); G06Q 50/02 (2013.01)] 33 Claims
OG exemplary drawing
 
1. A computer-implemented method for training a predictive model for crop management comprising:
collecting a first data set about at least one global agricultural factor from a remote data collection service;
collecting a second data set about at least one local agricultural factor from a local data collection device;
assimilating the first and second data sets into a local database to create a first training data set comprising the collected first set of data and the collected second set of data;
training a predictive model using the first training data set to produce one or more measurable outcomes for the crop;
collecting a third data set about at least one global vineyard factor from the remote data collection service;
collecting a fourth data set about at least one local vineyard factor from the local data collection device; and
predicting a measurable crop outcome using the trained predictive model in response to the collected third data set and the collected fourth data set.