Diagnosis and 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, crop characteristics, and observations and user input of harvest condition states

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 in-field dry-down of a food grains crop for planning a timing 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 at a specific location 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 1) aggregate the expected weather conditions with crop-specific information into an agricultural model of one or more physical and empirical characteristics impacting a rate of drying, configured to simulate an expected rate of drying of the food grains crop in a particular field, and 2) automatically develop an artificial intelligence model configured to analyze a specific in-field dry-down model by building a comprehensive dataset for the agricultural model of one or more physical and empirical characteristics impacting harvest operations of the food grains crop, and predict in-field dry-down of a crop with similar characteristics in any field at any selected time;
• and
• a harvest condition prediction module configured to predict in-field dry-down of the food grains crop generate one or more advisories of the predicted in-field dry-down in a harvest output condition profile.

A method of evaluating in-field dry-down of a mature small grain, coarse grain, or oilseed crop, 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 profile in-field dry-down of a food grains crop in a harvest advisory model configured to perform the steps of:
• predicting expected weather conditions impacting a rate of drying of the grain associated with the food grains crop at a specific location 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;
• simulating an expected rate of drying of the food grains crop in a particular field by applying the expected weather conditions, crop-specific information including crop type data, relative maturity data, planting data, and harvest data, and one or more sampled observations of actual moisture from a planted field at one or more times to an agricultural model of one or more physical and empirical characteristics impacting harvest operations of the food grains crop, the agricultural model comprising at least one of a crop-specific growth model, an in-field dry-down model, a soil model, and a land surface model;
• and
• modifying the simulation of the expected rate of drying using differences between the otherwise simulated expected rate of drying and the one or more sampled observations of actual moisture from a planted field at corresponding times, to generate a prediction of pre-in-field dry-down of the food grains crop in the particular field over time.

A method of evaluating in-field dry-down of a food grains crop, 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 profile in-field dry-down of the food grains crop in a harvest advisory model configured to perform the steps of:
• predicting expected weather conditions impacting a rate of drying of the grain associated with food grains crop at a specific location by applying weather information comprised of recent or current field-level weather data and extended-range weather forecast data to one or more predictive numerical weather models;
• automatically developing an artificial intelligence model configured to analyze a specific in-field dry-down by applying the expected weather conditions, crop metadata representing actual and/or realized performance of the food grains crop over a specified period of time, and one or more sampled observations of actual moisture from one or more fields and one or more crops, by building a comprehensive dataset for the agricultural model of one or more physical and empirical characteristics impacting harvest operations of the food grains crop to predict the in-field dry-down of a crop with similar characteristics in any field at any selected time.