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US 12,213,395 B2Granted
Monitoring and intelligence generation for farm field
Assignee6th Grain Corporation
InventorsMolly Elizabeth Brown, Min Feng, Vladimir Eskin
Life of this patent
Estimating the term…
Abstract
Various embodiments described herein provide monitoring and intelligence generation for one or more farm fields by: using remote sensing to monitor progress of a developing crop; comparing a user's field to another field in the area; providing cropped area extent estimates for a current season; using one or more disease risk models to determine disease risk (or disease pressure) with respect to a field; or some combination thereof.
Claims19
A method comprising:
- accessing, by a hardware processor, tile data describing a plurality of tile images for a geographic region over a period of time, the tile data comprising pixels and being generated based on satellite remote sensing data;
- generating, by the hardware processor and based on the tile data, farm field pixel data for a farm field of interest, the farm field pixel data describing a plurality of pixels for the farm field of interest over the period of time;
- generating, by the hardware processor and based on the farm field pixel data, vegetation index data for the farm field of interest by, for each individual pixel in the plurality of pixels:
- generating an initial time series of vegetation index values for the individual pixel over the period of time;
- and
- generating a reconstructed time series of vegetation index values for the individual pixel over the period of time by using a first machine learning model to generate a predicted vegetation index value for each vegetation index value in the initial time series of vegetation index values, the first machine learning model comprising a neural network model trained to reconstruct vegetation index values of all pixels of a field;
- generating, by the hardware processor, a crop growth curve for the farm field of interest based on the vegetation index data;
- generating, by the hardware processor, a crop growth band based on historical data that describes vegetation index values for one or more other farm fields in the geographic region;
- causing, by the hardware processor, presentation of the crop growth band and the crop growth curve on a client graphical user interface on a client device, the crop growth band and the crop growth curve being presented relative to each other for comparison;
- generating, using a second machine learning model, a simulated yield value for the field of interest based on the crop growth band;
- and
- generating, by the hardware processor, one or more alerts based on the simulated yield value for the field of interest, the one or more alerts being reviewable by a user, at least one alert of the one or more alerts providing actionable information to the user for adjusting management of the field of interest.
9 more elements
A non-transitory computer-readable storage medium including instructions that, when processed by a hardware processor, causes the hardware processor to perform operations comprising:
- accessing tile data describing a plurality of tile images for a geographic region over a period of time, the tile data comprising pixels and being generated based on satellite remote sensing data;
- generating, based on the tile data, farm field pixel data for a farm field of interest, the farm field pixel data describing a plurality of pixels for the farm field of interest over the period of time;
- generating, based on the farm field pixel data, vegetation index data for the farm field of interest by, for each individual pixel in the plurality of pixels:
- generating an initial time series of vegetation index values for the individual pixel over the period of time;
- and
- generating a reconstructed time series of vegetation index values for the individual pixel over the period of time, by using a first machine learning model to generate a predicted vegetation index value for each vegetation index value in the initial time series of vegetation index values, the first machine learning model comprising a neural network model trained to reconstruct vegetation index values of all pixels of a field;
- generating a crop growth curve for the farm field of interest based on the vegetation index data;
- generating a crop growth band based on historical data that describes vegetation index values for one or more other farm fields in the geographic region;
- causing presentation of the crop growth band and the crop growth curve on a client graphical user interface on a client device, the crop growth band and the crop growth curve being presented relative to each other for comparison;
- generating, using a second machine learning model, a simulated yield value for the field of interest based on the crop growth band;
- and
- generating one or more alerts based on the simulated yield value for the field of interest, the one or more alerts being reviewable by a user, at least one alert of the one or more alerts providing actionable information to the user for adjusting management of the field of interest.
9 more elements
A system comprising:
- a hardware processor;
- and
- a memory storing instructions that, when executed by the processor, cause the hardware processor to perform operations comprising:
- accessing tile data describing a plurality of tile images for a geographic region over a period of time, the tile data comprising pixels and being generated based on satellite remote sensing data;
- generating, based on the tile data, farm field pixel data for a farm field of interest, the farm field pixel data describing a plurality of pixels for the farm field of interest over the period of time;
- generating, based on the farm field pixel data, vegetation index data for the farm field of interest by, for each individual pixel in the plurality of pixels:
- generating an initial time series of vegetation index values for the individual pixel over the period of time;
- and
- generating a reconstructed time series of vegetation index values for the individual pixel over the period of time, by using a first machine learning model to generate a predicted vegetation index value for each vegetation index value in the initial time series of vegetation index values, the first machine learning model comprising a neural network model trained to reconstruct vegetation index values of all pixels of a field;
- generating a crop growth curve for the farm field of interest based on the vegetation index data;
- generating a crop growth band based on historical data that describes vegetation index values for one or more other farm fields in the geographic region;
- causing presentation of the crop growth band and the crop growth curve on a client graphical user interface on a client device, the crop growth band and the crop growth curve being presented relative to each other for comparison;
- generating, using a second machine learning model, a simulated yield value for the field of interest based on the crop growth band;
- and
- generating one or more alerts based on the simulated yield value for the field of interest, the one or more alerts being reviewable by a user, at least one alert of the one or more alerts providing actionable information to the user for adjusting management of the field of interest.
12 more elements
Dependencies are inferred from the claim text.