Harvest advisory modeling using field-level analysis of weather conditions and observations and user input of harvest condition states and tool for supporting management of farm operations in precision agriculture

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 of evaluating agricultural conditions 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 profile a harvest condition for an agricultural product to be harvested in a harvest advisory model configured to perform the steps of: predicting expected weather conditions at a specific location in a temporal harvest window by applying weather information comprised of current field-level weather data and extended-range weather data to one or more predictive numerical weather models; simulating the harvest condition by applying the expected weather conditions and crop-specific information including crop type data, relative maturity data, planting data, and harvest data to an agricultural model of one or more physical and empirical characteristics impacting harvest operations of the agricultural commodity, the agricultural model comprising at least one of a crop-specific growth model, a soil model, and a land surface model; and modifying the simulation of the harvest condition using differences between the simulated harvest condition and additional data representing sampled observations at or near the specific location at corresponding times, to generate a prediction of the harvest condition during the temporal harvest window.