Patent ID: 11880430
Assignee: CIBO TECHNOLOGIES, INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G  A | IPC A  G

Claim 14:
15. A computer program product for predicting agricultural management zones 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; and
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.