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

Claim 0:
1. A computer-implemented method for determining agricultural irrigation management practices for use within a current growing year, the computer-implemented method comprising:
retrieving 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;
retrieving 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;
employing the second set of records as inputs to train a first deep learning convolutional neural network to generate the first set of records and using parameters generated during training to configure a trained first deep learning convolutional neural network for execution;
configuring a second deep learning convolutional neural network using parameters corresponding to early layers of the trained first deep learning convolutional neural network;
retrieving 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;
employing 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 using parameters generated during training to configure a trained second deep learning convolutional neural network for execution;
retrieving a fifth set of records from a current imagery database, wherein the fifth set of records comprises corresponds to a third geographic region, and wherein the fifth set of records corresponds to the second time period and the current growing year;
using the fifth set of records as inputs and executing the trained second deep learning convolutional neural network to generate predicted agricultural management zones for the current growing year; and
aggregating the fifth set of record into vegetative indices for parcels within the third geographic region and processing 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.