| 摘要 |
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. |
| 主权项 |
1. A method comprising:
ingesting, as input data, weather information, crop-specific information for a stored grains crop, and grain storage facility information,
the weather information including recent and current field-level weather data and extended-range weather forecast data,the crop-specific information including at least one of crop type data, crop post-maturity dry-down characteristics, crop airflow characteristics, and targeted crop moisture or temperature thresholds, andthe grain storage facility information including at least one of grain storage facility dimensions data and grain storage facility airflow data; 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 moisture content of the stored grains crop relative to impacts of time-varying weather conditions, in-bin drying characteristics, and existing crop moisture conditions on an equilibrium moisture content of a grains crop stored in a grain storage facility, by
1) diagnosing and predicting expected weather conditions impacting energy requirements for in-bin drying of the stored grains crop at a specific location,2) applying the expected weather conditions, the crop-specific information, and the grain storage facility information to an agricultural model of one or more physical and empirical characteristics impacting a rate of forced-air ventilation of the stored grains crop to simulate time-varying moisture content of the stored grains crop at specific times and predict a rate of forced-air ventilation for the stored grains crop at the specific location over time, and3) applying facility metadata representing actual and/or realized performance characteristics of the grain storage facility, 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 of the stored grains crop; and generating, as output data, a rate of forced-air ventilation to achieve a desired moisture content in a harvest output condition profile. |
