Prediction of in-field dry-down of a mature small grain, coarse grain, or oilseed crop using field-level analysis and forecasting of weather conditions and crop characteristics including sampled moisture content

Patent metadata

Field	Value
Publication number	US-10255390-B2
Application number	US-201615345843-A
Country	US
Kind code	B2
Filing date	Nov 8, 2016
Priority date	Jan 23, 2015
Publication date	Apr 9, 2019
Grant date	Apr 9, 2019

Abstract

A modeling framework for evaluating the impact of weather conditions on farming and harvest operations applies real-time, field-level weather data and forecasts of meteorological and climatological conditions together with user-provided and/or observed feedback of a present state of a harvest-related condition to agronomic models and to generate a plurality of harvest advisory outputs for precision agriculture. A harvest advisory model simulates and predicts the impacts of this weather information and user-provided and/or observed feedback in one or more physical, empirical, or artificial intelligence models of precision agriculture to analyze crops, plants, soils, and resulting agricultural commodities, and provides harvest advisory outputs to a diagnostic support tool for users to enhance farming and harvest decision-making, whether by providing pre-, post-, or in situ-harvest operations and crop analyzes.

First claim

The invention claimed is:

1. A method comprising:
ingesting, as input data, weather information and crop-specific information for a grain crop to be harvested, the weather information including recent and current field-level weather data and extended-range weather forecast data, and the crop-specific information at least including a sampled moisture content of the grain crop collected by sensors associated with agricultural equipment in a particular field hosting the grain crop to be harvested, the sensors configured to gather data related to in-field dry down directly from the particular field, the sampled moisture content representing an amount of water present in the grain crop reaped as a percentage of weight;
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 processor, the data processing modules configured to project an in-field dry-down of the grain crop over time for a planning of harvest operations by
1) predicting expected weather conditions impacting a rate of drying of the grain crop in the particular field,
2) developing an agricultural model that applies the expected weather conditions and one or more physical and empirical characteristics of a grain crop from the crop-specific information to perform one or more simulations to forecast changes in an in-field moisture content of the grain crop over time in the particular field, and
3) continually augmenting the forecasted changes in the in-field moisture content over time by performing additional simulations based on the sampled moisture content of the grain crop collected by the sensors associated with the agricultural equipment in the particular field as a growing season progresses, updated field-level weather observations, and updated weather forecast data for a geographical area including the particular field, to predict expected times for the grain crop to reach one or more in-field moisture content thresholds;

generating, as output data, a harvest condition profile representing one or more predictions of the in-field dry-down of the grain crop in the particular field based on the forecasted changes in the in-field moisture content over time; and
developing a harvest schedule for performing one or more agricultural activities from the harvest condition profile in the particular field, wherein a user performs the one or more agricultural activities based on the harvest schedule, or an automated performance of the one or more agricultural activities by an agricultural vehicle is controlled based on the harvest schedule.

2. The method of claim 1 , further comprising generating one or more advisories for at least one of a harvesting machine in the particular field, a user onboard the harvesting machine, and a user involved in harvest operations in the particular field, based on the harvest condition profile.

3. The method of claim 2 , further comprising applying the harvest condition profile to a harvest advisory tool configured to provide the one or more advisories to the at least one of a harvesting machine in the particular field, a user onboard the harvesting machine, and a user involved in harvest operations in the particular field.

4. The method of claim 2 , wherein the one or more advisories include an advisory that the grain crop will experience further in-field drying over a specified time period, an advisory that the grain crop will not experience further in-field drying over a specified time period, and an advisory that the grain crop will reach a desired moisture content in an anticipated harvest window.

5. The method of claim 1 , wherein the crop-specific information further includes one or more of anticipated temporal harvest windows, crop growth stage data, crop relative maturity data, crop planting depth and row spacing data, crop planting date data, crop post-maturity dry-down characteristics, and targeted harvest crop moisture or temperature thresholds for the grain crop.

6. The method of claim 1 , wherein the modeling further comprises applying the weather information to one or more predictive numerical weather models to generate the prediction of expected weather conditions.

7. The method of claim 1 , wherein the applying the expected weather conditions and the crop-specific information to an agricultural model that applies one or more physical and empirical characteristics of the grain crop further comprises simulating the rate of drying of the grain crop in a particular field, identifying differences in one or more simulations of the rate of drying with the sampled moisture content at corresponding times, and modifying the one or more simulations based on the differences between the otherwise simulated rate of drying and the sampled moisture content at the corresponding times.

8. The method of claim 1 , further comprising applying the expected weather conditions, the sampled moisture content, and crop metadata representing actual and/or realized performance of the grain crop over a specified time period to automatically develop an artificial intelligence model configured to analyze a specific in-field dry-down of grain crop by building a comprehensive harvest condition dataset for the agricultural model that applies one or more physical and empirical characteristics of a grain crop to forecast changes in an in-field moisture content of a grain crop with similar characteristics in any field at any selected time.

9. The method of claim 1 , wherein the grain crop is at least one of a mature small grain, coarse grain, or oilseed crop.

10. A method of evaluating moisture content of a grain crop to support harvest operations, 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 project an in-field dry-down of the grain crop over time for a planning of harvest operations in a harvest advisory model configured to perform the steps of:
predicting expected weather conditions impacting a rate of drying of the grain crop in a particular field hosting the grain crop at one or more temporal harvest windows by applying weather information comprised of recent and current field-level weather data and extended-range weather forecast data to one or more predictive numerical weather models;
developing an agricultural model that applies the expected weather conditions and one or more physical and empirical characteristics of a grain crop from crop-specific information that at least includes a sampled moisture content of the grain crop collected by sensors associated with agricultural equipment in the particular field, the sensors configured to gather data related to an in-field dry-down directly from the particular field, to model the rate of drying of the grain crop over time in the particular field by performing one or more simulations, the sampled moisture content representing an amount of water present in the grain crop reaped as a percentage of weight;
forecasting changes in an in-field moisture content of the grain crop over time in the particular field as the one or more temporal harvest windows approach from the rate of drying; and
continually augmenting the forecasted changes in the in-field moisture content over time by performing additional simulations based on the sampled moisture content of the grain crop collected by the sensors associated with the agricultural equipment in the particular field as a growing season progresses, updated field-level weather observations, an

Assignees

• Iteris Inc
• Clearag Inc

Inventors

• Mewes John J
• Salentiny Dustin M

Classifications

• G01W1/10

  Devices for predicting weather conditions (computers per se G06; display devices G09) · CPC title

• A01B79/005

  Precision agriculture · CPC title

• G05B13/048

  using a predictor · CPC title

• G06Q50/02

  Agriculture; Fishing; Forestry; Mining · CPC title

• A01D91/00

  Methods for harvesting agricultural products · CPC title