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

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 food grains 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 planting 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 in-field dry-down of the food grains crop for planning a timing of harvest operations by
1) predicting expected weather conditions impacting the rate of drying of the food grains crop in the planted field at a specific location,2) applying the expected weather conditions and the crop-specific information to an agricultural model of one or more physical and empirical characteristics impacting harvest operations of the food grains crop to simulate an expected rate of drying of the food grains crop in a particular field, and3) identifying differences in the simulation of expected rate of drying with one or more sampled observations of actual moisture from a planted field at corresponding times, and modifying the simulation based on the differences between the otherwise simulated rate of drying and the one or more sampled observations of actual moisture content at the corresponding times to predict an in-field dry-down of the food grains crop in the particular field over time; and generating, as output data, one or more advisories representing the predicted in-field dry-down in a harvest output condition profile.