Modeling of costs associated with in-field and fuel-based drying of an agricultural commodity requiring sufficiently low moisture levels for stable long-term crop storage using field-level analysis and forecasting of weather conditions, grain dry-down model, facility metadata, and observations and user input of harvest condition states

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 analyzes.

主权项

1. A method comprising:
ingesting, as input data, weather information and crop-specific information for a 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 including at least one of crop type data and harvest 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 unit costs of drying an agricultural commodity requiring sufficiently low moisture levels for stable long-term storage, by
1) predicting expected weather conditions relative to crop moisture levels during in-field dry-down of a crop in a particular field and for a drying facility using at least one of fuel-based drying or forced-air mechanical drying;2) applying the expected weather conditions and the crop-specific information to at least one of an agricultural model of one or more physical and empirical characteristics impacting in-field dry-down of an agricultural commodity, and an artificial intelligence model acting as a specific tailored drying model, to simulate a combined impact of the expected weather conditions, the crop-specific information, and the field-specific information on crop moisture levels,3) applying one or more sampled observations from a planted field at corresponding times and facility metadata representing actual and/or realized performance characteristics of a crop drying facility using at least one of fuel-based drying or forced-air mechanical drying to the at least one agricultural model and the artificial intelligence model to estimate a per-unit, time-varying cost of drying the crop, and4) predicting a time-varying overall forced-air or fuel-based cost per unit of mass or volume of grain, generating, as output data, one or more advisories representing a prediction of the overall cost of grain drying per unit of mass or volume of the agricultural commodity in a harvest output condition profile.