In-Bin Grain Drying Control Using Weather-Based Moisture Forecasts
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Solution Overview
Problem
Current agricultural models lack the ability to accurately diagnose and predict field-level weather conditions, leading to inefficiencies in harvest operations due to limited geographic representativeness and insufficient utilization of weather information, resulting in suboptimal crop management and revenue loss.
Innovation Solution
A system and method that applies real-time, field-level weather simulation and prediction to precision agriculture models, combining location-tagged data communication and user feedback to generate harvest advisory outputs, utilizing physical, empirical, and artificial intelligence models to analyze crop, soil, and atmospheric conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If in-situ monitoring of field conditions is conducted regularly, then measurement precision of field-level weather conditions is improved, but device complexity and labor resources increase significantly
Solution Approach 1:
The patent uses satellite imagery and remote sensing data as copies or proxies for direct field measurements. Instead of deploying complex in-situ monitoring equipment throughout the field, the system captures remote signals (satellite images, weather radar data) that represent field conditions without requiring physical presence or complex ground-based instrumentation.
Solution Approach 2:
The patent introduces agronomic models and weather simulation models as intermediaries between raw weather data and harvest decision-making. These models process and interpret complex weather information, converting it into actionable insights about crop moisture levels, harvest timing, and field conditions without requiring direct measurement of all parameters.
2Manufacturing precision
If harvest operations are delayed to achieve optimal crop moisture levels, then manufacturing precision of harvest quality is improved, but loss of time and productivity increase
Solution Approach 1:
The patent performs preliminary weather simulation and crop moisture prediction before harvest operations begin. By running agronomic models in advance with forecasted weather data, the system determines optimal harvest windows and prepares harvest plans ahead of time, allowing farmers to schedule harvest operations at the precise moment when moisture levels will be optimal without unnecessary delays.
Solution Approach 2:
The patent incorporates feedback loops where actual field measurements and harvest results are used to refine and recalibrate agronomic models. This continuous feedback improves the accuracy of moisture level predictions over time, enabling more precise harvest timing decisions that balance quality requirements with time efficiency.
3Manufacturing precision
If forced-air ventilation is used to control grain moisture levels, then manufacturing precision of moisture control is improved, but use of energy increases significantly
Solution Approach 1:
The patent employs dynamic, adaptive drying strategies where forced-air ventilation parameters (airflow rate, temperature, duration) are adjusted in real-time based on grain moisture measurements and weather forecasts. The system transitions between different drying intensities and modes, using high-energy forced-air drying only when necessary to meet moisture targets, rather than applying constant maximum energy input.
Solution Approach 2:
The patent changes physical parameters of the drying process (air temperature, airflow velocity, relative humidity) based on real-time grain moisture content and ambient weather conditions. By dynamically adjusting these parameters, the system achieves precise moisture control while minimizing energy consumption, using higher energy input only when environmental conditions are unfavorable for natural drying.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances decision-making in harvest operations by predicting optimal harvest times, reducing yield loss, and improving the efficiency of drying processes, while providing guidance on soil and crop conditions, thereby increasing profitability and long-term viability of farm operations.
Implementation Method 1
a dielectric grain moisture level measurement system
Implementation Method 2
applying real-time, field-level weather simulation and prediction to one or more agricultural models
Implementation Method 3
control moisture level based on anticipated atmospheric conditions and forecast time periods of energy usage to achieve desired rate of grain moisture change through forced-air ventilation
Data Source
AI summary
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.