Harvest advisory modeling using field-level analysis of weather conditions, observations and user input of harvest condition states, wherein a predicted harvest condition includes an estimation of standing crop dry-down rates, and an estimation of fuel costs

• US 9,076,118 B1
• Filed: 01/23/2015
• Issued: 07/07/2015
• Est. Priority Date: 01/23/2015
• Status: Active Grant

• Current Assignee: DTN, LLC (TBG AG)
• Original Assignee: Iteris Incorporated
• Inventors: Mewes, John J., Salentiny, Dustin M.
• Primary Examiner(s): VINCENT, DAVID ROBERT
• Application Number:
  US14/603,378
• Time in Patent Office: 165 Days
• Field of Search: 706/12, 706/45, 706/62
• US Class Current: 1/1
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06Q 10/0631 Resource planning, allocation, distributing or scheduling for enterprises or organisations
• CPC Class Codes: G06Q 10/067 Enterprise or organisation modelling
• CPC Class Codes: G06Q 50/02 Agriculture; Fishing; Forestry; Mining
• CPC Class Codes: Y02A 40/10 in agriculture

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 analyses.

Claims

1. A method comprising:

ingesting, as input data, weather information and crop-specific information for an agricultural commodity to be harvested, the weather information including at least one of current field-level weather data and extended-range weather data, and the crop-specific information including at least one of crop type data and planting data, and harvest data that at least includes a temporal harvest window;

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 profile a harvest condition for the agricultural commodity to be harvested, by

     1) predicting expected weather conditions at a specific location and within the temporal harvest window,

     2) aggregating the expected weather conditions with the crop-specific information into an agricultural model of one or more physical and empirical characteristics impacting harvest operations of the agricultural commodity configured to simulate the harvest condition, and

     3) identifying differences in the simulation of the harvest condition with additional data representing sampled observations at or near the specific location at corresponding times, and modifying the simulation of the harvest condition based on the differences between the simulated harvest condition and the additional data to predict the harvest condition during the temporal harvest window; and

generating, as output data, one or more advisories of the predicted harvest condition in a harvest condition output profile, the predicted harvest condition including at least one of an estimation of standing crop dry-down rates, a prediction of anticipated temporal harvest windows, an assessment of indicators of plant suitability for harvest, an estimation of fuel costs for post-harvest drying, and an estimation of loss of field workability due to frost formation in soil prior to post-harvest tillage.

2. The method of claim 1, further comprising applying the harvest output condition profile to a harvest advisory tool configured to provide the one or more advisories representing the predicted harvest conditions to a user.

3. The method of claim 1, wherein the crop-specific information further includes one or more of crop relative maturity data, crop planting depth and row spacing data, crop post-maturity dry-down characteristics, targeted harvest crop moisture or temperature thresholds, facility storage dimensions data, crop storage airflow data, and fuel-based grain dryer facility characteristics.

4. 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.

5. The method of claim 1, further comprising ingesting the additional data representing sampled observations at or near the specific location at corresponding times, the additional data including physical, empirical or observed agricultural information and field and soil information, wherein the physical, empirical or observed agricultural information and field and soil information includes at least one of sampled crop moisture content and metadata related to crop, seed, soil, and facility data, sampled soil temperature, sampled soil moisture content, and current and/or forecasted soil conditions.

6. The method of claim 1, wherein the agricultural model of one or more physical and empirical characteristics impacting harvest operations of the agricultural product includes at least one of a crop-specific growth model, a soil model, and a land surface model.

7. The method of claim 1, further comprising applying the expected weather conditions and the additional data representing sampled observations at or near the specific location at corresponding times to automatically develop an artificial intelligence model configured to analyze a specific harvest condition model by building a comprehensive harvest condition dataset for the agricultural model of one or more physical and empirical characteristics impacting harvest operations of the agricultural commodity, and applying the artificial intelligence model to predict the harvest condition during the temporal harvest window.

8. A system comprising:

a computing environment including at least one computer-readable storage medium having program instructions stored therein and a computer processor operable to execute the program instructions to profile a harvest condition for an agricultural commodity to be harvested in a harvest advisory model within a plurality of data processing modules, the plurality of data processing modules including;

a weather modeling module configured to diagnose and predict expected weather conditions at a specific location in a temporal harvest window by applying weather information including current field-level weather data and extended-range weather data to one or more predictive numerical weather models;

one or more modules configured to

     1) aggregate the expected weather conditions with crop-specific information into an agricultural model of one or more physical and empirical characteristics impacting harvest operations of the agricultural commodity configured to simulate the harvest condition, and

     2) automatically develop an artificial intelligence model configured to analyze the harvest condition by building a comprehensive harvest condition dataset for the agricultural model of one or more physical and empirical characteristics impacting harvest operations of the agricultural product, and predict the harvest condition for a crop with similar characteristics in any field at a selected time; and

a harvest condition prediction module configured to apply the artificial intelligence model to predict the harvest condition during a harvest window and generate one or more advisories of the predicted harvest condition in a harvest condition output profile.

9. The system of claim 8, wherein the predicted harvest condition includes at least one of an estimation of standing crop dry-down rates, a prediction of anticipated temporal harvest windows, an assessment of indicators of plant suitability for harvest, an estimation of fuel costs for post-harvest drying, and an estimation of loss of field workability due to frost formation in soil prior to post-harvest tillage.

10. The system of claim 8, wherein the harvest output condition profile is applied to a diagnostic support tool configured to provide the one or more advisories representing the predicted harvest conditions to a user.

11. The system of claim 8, wherein the one or more modules includes a physical and empirical modeling module, within which differences in the simulation of the harvest condition with additional data representing sampled observations at or near the specific location at corresponding times are identified, wherein the harvest advisory model performs a modification of the simulation of the harvest condition based on the differences between the simulated harvest condition and the additional data to predict the harvest condition during the temporal harvest window.

12. The system of claim 8, wherein the crop-specific information further includes one or more of crop relative maturity data, crop planting depth and row spacing data, crop post-maturity dry-down characteristics, targeted harvest crop moisture or temperature thresholds, facility storage dimensions data, crop storage airflow data, and fuel-based grain dryer facility characteristics.

13. The system of claim 8, wherein the additional data representing sampled observations at or near the specific location at corresponding times includes physical, empirical or observed agricultural information and field and soil information, wherein the physical, empirical or observed agricultural information and field and soil information includes at least one of sampled crop moisture content and metadata related to crop, seed, soil, and facility data, sampled soil temperature, sampled soil moisture content, and current and/or forecasted soil conditions.