A method of evaluating moisture content of a stored 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 moisture content of a stored grains crop in a harvest advisory model configured to perform the steps of:
- diagnosing and predicting expected weather conditions impacting the moisture content of the stored grains crop in a crop storage facility and energy requirements for in-bin drying of the stored 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 time-varying moisture content of the stored grains crop at specific times in an agricultural model of one or more physical and empirical characteristics impacting a rate of forced-air ventilation for the stored grains crop by aggregating the expected weather conditions with crop-specific information and grain storage facility information, the agricultural model comprising at least one of deterministic and stochastic grain drying models based on at least one of characteristics of a drying process and statistical models that parameterize drying characteristics of a specific crop to be stored;
- identifying differences between the simulation of time-varying moisture content and one or more sampled observations of actual moisture content representing existing moisture conditions from the grain storage facility at corresponding times;
- modifying the simulation based on the identified differences between the otherwise simulated time-varying moisture content and the one or more sampled observations of actual moisture content at the corresponding times to generate a predicted rate of forced-air ventilation for the stored grains crop at the grain storage facility over time;
- and
- applying facility metadata representing actual and/or realized performance characteristics of the grain storage facility and the expected weather conditions, the crop-specific information, and the grain storage facility information, to an artificial intelligence model of drying a harvested grain crop to automatically develop a specific energy usage cost model by building a comprehensive dataset of one or more physical and empirical characteristics impacting the rate of forced-air ventilation for the stored grains crop.