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

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, 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;
• 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.

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 develop an agricultural model that aggregates the expected weather conditions and one or more physical and empirical characteristics of a grain crop with crop-specific information at least including 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, and the sampled moisture content representing an amount of water present in the grain crop reaped as a percentage of weight, the agricultural model configured 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 continually augment 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;
• 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;
• and
• an output module configured to develop 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.