Patent ID: 11934489
Assignee: CIBO TECHNOLOGIES, INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 14:
15. A computer program product for determining agricultural irrigation management practices for use within a current growing year, the computer program product comprising:
a computer readable non-transitory medium having computer readable program code stored thereon, the computer readable program code comprising:
program instructions to retrieve a first set of records from a historical cropland data layer database, wherein the first set of records corresponds to randomly sampled areas of a first geographic region taken over a first time period for a first number of years;
program instructions to retrieve a second set of records from a historical imagery database, wherein the second set of records corresponds to the randomly sampled areas of the first geographic region, the first time period, and the first number of years;
program instructions to employ the second set of records as inputs to train a first deep learning convolutional neural network to generate the first set of records and to use parameters generated during training to configure a trained first deep learning convolutional neural network for execution;
program instructions to configure a second deep learning convolutional neural network using parameters corresponding to early layers of the trained first deep learning convolutional neural network;
program instructions to retrieve a third set of records and a fourth set of records from an annotated imagery database, wherein the third set of records comprises unannotated image versions corresponding to a second geographic region, and wherein the fourth set of records comprises annotated image versions corresponding to the second geographic region, and wherein the annotated image versions comprise annotations indicative of management zones, and wherein the third and fourth sets of records correspond to a second time period for a second number of years;
program instructions to employ the third set of records as inputs to train upper layers of the second deep learning convolutional neural network to generate the fourth set of records and to use parameters generated during training to configure a trained second deep learning convolutional neural network for execution;
program instructions to use the fifth set of records as inputs and to execute the trained second deep learning convolutional neural network to generate predicted agricultural management zones for the current growing year; and
program instructions to aggregate the fifth set of record into vegetative indices for parcels within the third geographic region, and to process the vegetative indices against maturity curves and rainfall data over the second time period for the current growing year to infer an irrigation practice for each of the predicted agricultural management zones.