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.
Please help me write a proper abstract based on the patent claims. CLAIM: 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 agricultural equipment in a particular field hosting the grain crop to be harvested, 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, and 2) applying the expected weather conditions and the crop-specific information to an 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 the grain crop over time in the particular field; and 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. 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; applying the expected weather conditions and crop-specific information that at least includes a sampled moisture content of the grain crop collected by agricultural equipment in the particular field to an agricultural model that applies one or more physical and empirical characteristics impacting harvest operations of the grain crop to model the rate of drying of the grain crop over time in the particular field, the sampled moisture content representing an amount of water present in the grain crop reaped as a percentage of weight; and 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. 11. The method of claim 10, further comprising generating 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. 12. The method of claim 11, 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. 13. The method of claim 12, 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. 14. The method of claim 12, further comprising applying the harvest condition profile to a diagnostic support 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, based on the harvest condition profile. 15. The method of claim 10, further comprising simulating the rate of drying in the agricultural model that applies one or more physical and empirical characteristics of a grain crop, and modifying one or more simulations of the rate of drying using differences between the otherwise simulated rate of drying and the sampled moisture content of the grain crop collected by agricultural equipment in the particular field at corresponding times. 16. The method of claim 10, wherein the crop-specific information further includes one or more of 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. 17. The method of claim 10, further comprising automatically developing or more artificial intelligence artificial models configured to analyze a specific in-field dry-down of a grain crop by building a comprehensive harvest condition dataset for the agricultural model of one or more physical and empirical characteristics 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. 18. The method of claim 10, wherein the grain crop is at least one of a mature small grain, coarse grain, or oilseed crop. 19. 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 project an in-field dry-down of the grain crop over time for a planning of harvest operations by performing a harvest advisory model within a plurality of data processing modules, the plurality of data processing modules including: a weather modeling module configured to predict expected weather conditions impacting a rate of drying of the grain crop in a particular field hosting the grain crop to be harvested by applying weather information including recent and current field-level weather data and extended-range weather forecast data to one or more predictive numerical weather models; one or more modules configured to aggregate the expected weather conditions with crop-specific information at least including a sampled moisture content of the grain crop collected by agricultural equipment in the particular field, the sampled moisture content representing an amount of water present in the grain crop reaped as a percentage of weight into an agricultural model that applies one or more physical and empirical characteristics of a grain crop, configured to forecast changes in an in-field moisture content of the grain crop over time in the particular field; and a harvest condition module configured to generate 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 of the grain crop over time, and one or more advisories to 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. 20. The system of claim 19, wherein the harvest condition profile is applied to a diagnostic support tool configured to provide the one or more advisories to one or more of a user performing harvest operations, and agricultural equipment performing harvest operations. 21. The system of claim 19, 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. 22. The system of claim 19, 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. 23. The system of claim 19, wherein the one or more modules are further configured to simulate the rate of drying in the agricultural model that applies one or more physical and empirical characteristics of a grain crop, and modify one or more simulations of the rate of drying using differences between the otherwise simulated rate of drying and the sampled moisture content of the grain crop collected by agricultural equipment in the particular field at corresponding times. 24. The system of claim 19, wherein the one or more modules are further configured to automatically develop an artificial intelligence model configured to analyze a specific in-field dry-down of a grain crop by building a comprehensive dataset for the agricultural model that applies one or more physical and empirical characteristics of a grain crop, and predict in-field dry-down of a crop with similar characteristics in any field at any selected time. 25. The system of claim 19, wherein the grain crop is at least one of a mature small grain, coarse grain, or oilseed crop.