Customized land surface modeling for irrigation decision support in a crop and agronomic advisory service in precision agriculture

Patent metadata

Field	Value
Publication number	US-9880537-B2
Application number	US-201615229968-A
Country	US
Kind code	B2
Filing date	Aug 5, 2016
Priority date	Aug 5, 2015
Publication date	Jan 30, 2018
Grant date	Jan 30, 2018

Abstract

An irrigation modeling framework in precision agriculture utilizes a combination of weather data, crop data, and other agricultural inputs to create customized agronomic models for diagnosing and predicting a moisture state in a field, and a corresponding need for, and timing of, irrigation activities. Specific combinations of various agricultural inputs can be applied, together with weather information to identify or adjust water-related characteristics of crops and soils, to model optimal irrigation activities and provide advisories, recommendations, and scheduling guidance for targeted application of artificial precipitation to address specific moisture conditions in a soil system of a field.

First claim

The invention claimed is:

1. A method, comprising:
ingesting, as input data, weather and climatological information that includes at least one of historical field-level weather data and extended-range weather forecast data, crop-specific information relative to a particular field, and soil information including data relating to crop and soil management practices that reflect at least one of prior tillage practices and prior moisture, temperature, and drainage conditions within a soil system in the particular field;
modeling the input data in a plurality of data processing modules within a computing environment in which the plurality of data processing modules are executed in conjunction with at least one specifically-configured processor, the data processing modules configured to assess a moisture state, a temperature state, and a drainage state of the soil system in the particular field, by
1) diagnosing and predicting expected weather conditions at or near the particular field from the weather and climatological information, 2) aggregating the expected weather conditions with the crop-specific information into coupled crop and soil simulation models of one or more physical and empirical characteristics impacting the moisture state in the soil system that form a land surface model customized to specific crop-soil interactions within the soil system in the particular field, and configured to simulate moisture, temperature and drainage properties at multiple depths over time in the soil system, and soil-water characteristics in a crop root zone relative to one or more crop growth stages of a crop in the particular field, for analyzing crop root properties within the soil system and predicting actual crop moisture use and variations in soil moisture conditions, soil temperature conditions, and soil drainage conditions, the soil-water characteristics including crop water usage, a depth profile of where water is extracted from by the crop at the one or more growth stages, adequacy of water supply at the soil depth, and an impact of prior irrigation activity on the moisture state, and 3) inferring at least one of thermal and hydraulic conductivity, root depth, and root distribution density at different depths of the soil system from one or more observations of in-field soil moisture, temperature, and drainage properties to adjust one or more parameters in the land surface model relating to the specific crop-soil interactions for performing one or more additional simulations of the moisture, temperature and drainage properties at multiple depths in the soil system; and
generating, as output data, a root zone moisture profile representing the moisture state of the soil system in the particular field over time for the one or more crop growth stages from the simulated moisture, temperature and drainage properties, and soil-water characteristics.

2. The method of claim 1 , further comprising applying the root zone moisture profile to an irrigation advisory tool configured to provide one or more irrigation recommendations to a user.

3. The method of claim 2 , wherein the irrigation advisory tool is further configured to generate one or more advisories for the particular field, the one or more advisories including an indicator of soil water loss from evaporation and evapotranspiration, an indicator of forecasted precipitation, an indicator of one or both of cost reduction and waste-of-water reduction in geographical locations affected by drought, an indicator of irrigation requirements for achieving crop temperature and crop moisture thresholds, and an indicator of environmental impact from runoff of one or both of fertilizers and chemicals.

4. The method of claim 2 , wherein the particular field is represented as one or more management zones.

5. The method of claim 4 , wherein the one or more irrigation recommendations include a site-specific variable rate irrigation recommendation representing an adjustable application of water at the one or more management zones, and a dynamic, crop-specific variable rate irrigation recommendation representing an adjustable application of water for different crops planted at the one or more management zones.

6. The method of claim 1 , wherein the soil-water characteristics further include an amount of water applied to the soil system through effective rainfall and irrigation, an amount of water that is retained in a dynamically-growing root zone, and an amount of water depleted from the dynamically-growing root zone.

7. The method of claim 1 , wherein the crop-specific information further includes one or more of crop type data, planting data, crop growth data, growth stage-dependent parameters indicative of the one or more crop growth stages, crop relative maturity data, crop planting depth and row spacing data, crop post-maturity dry-down characteristics, and targeted crop moisture or temperature thresholds.

8. The method of claim 1 , wherein the input data further includes irrigation-specific information relative to past irrigation activity for the particular field, the irrigation-specific information including data representing an amount of water applied to the particular field, and data representing a water delivery mechanism, the water delivery mechanism including one or more of flood irrigation, drip irrigation, or spray irrigation.

9. The method of claim 1 , wherein the input data further includes soil information representative of the soil system in the particular field.

10. The method of claim 1 , wherein the input data further includes field-level remotely-sensed imagery data of the particular field.

11. The method of claim 1 , further comprising automatically developing an artificial intelligence soil-water model to analyze time-varying soil-water characteristics of a soil system, by building a comprehensive dataset for the coupled crop and soil simulation models of one or more physical and empirical characteristics impacting the moisture state in the soil system and applying the artificial intelligence soil-water model to predict the moisture state in a soil system in any field at any selected time.

12. A method, comprising:
within a computing environment comprised of a computer processor and at least one computer-readable storage medium operably coupled to the computer processor and having program instructions stored therein, the computer processor being operable to execute the program instructions to assess a moisture state, a temperature state, and a drainage state of a soil system in the particular field in an irrigation advisory model configured to perform the steps of:
predicting expected weather conditions impacting moisture conditions in a crop root zone in the soil system in the particular field, by applying weather and climatological information comprised of historical field-level weather data and extended-range weather forecast data to one or more predictive numerical weather models;
simulating moisture, temperature and drainage properties at multiple depths over time in the soil system, and soil-water characteristics in the crop root zone relative to one or more crop growth stages of a crop in the particular field to analyze crop root properties within the soil system and predict actual crop moisture use and variations in soil moisture conditions, soil temperature conditions, and soil drainage conditions, by applying the expected weather conditions, crop-specific information relative to the particular field, and soil information including data relating to crop and soil management practices that reflect at least one of prior tillage practices and prior moisture, temperature, and drainage conditions within the soil system in the particular field to

Assignees

• Iteris Inc
• Clearag Inc

Inventors

• Mewes John J
• Hale Robert C

Classifications

• G05B17/02

  electric · CPC title

• A01G25/167 Primary

  Control by humidity of the soil itself or of devices simulating soil or of the atmosphere; Soil humidity sensors · CPC title

• A01G25/16

  Control of watering · CPC title

• G05B19/042 Primary

  using digital processors (G05B19/05 takes precedence) · CPC title

• G05B2219/23005

  Expert design system, uses modeling, simulation, to control design process · CPC title