CPC G06N 7/01 (2023.01) [G06N 3/0464 (2023.01)] | 19 Claims |
1. A computer-implemented method of forecasting and/or analyzing crop states based on at least one data source, the method comprising:
receiving seasonal image data from at least one source, wherein the seasonal image data is associated with at least one agricultural field;
processing the seasonal image data using a Bayesian framework, wherein the Bayesian framework comprises one or more crop models configured to predict, based on the seasonal image data, one or more probabilities indicative of at least one crop state;
updating at least one crop model of the Bayesian framework based on said one or more probabilities; and
outputting a forecast of said at least one crop state based on said one or more probabilities, wherein updating the Bayesian framework, further comprises
receiving the one or more set(s) of weighted confidence predictions, associated with each crop model;
determining whether the received one or more set(s) of weighted confidence predictions is within a predetermined range;
predicting said one or more probabilities based on the determination;
if the received information is not within a predetermined range, determining a separate set of weighted confidence information different from the received one or more set(s) of weighted confidence predictions; and
predicting said one or more probabilities based on the received one or more set(s) of the weighted confidence information and the second set of weighted confidence information.
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