Weather Simulation for Crop Dry-Down Cost Prediction
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Solution Overview
Problem
Current agricultural models lack the precision to diagnose and predict field-level weather conditions effectively, leading to inefficiencies in harvest operations due to limited geographic representativeness and inadequate weather data integration, 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 environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time field-level weather simulation and prediction is applied to precision agriculture models, then measurement precision of weather conditions is improved, but device complexity increases
Solution Approach 1:
The patent introduces a weather simulation and prediction system as an intermediary layer between raw weather data sources and agricultural decision-making processes. This intermediary integrates location-tagged data, user feedback, and multiple modeling approaches (physical, empirical, AI) to translate complex weather information into actionable harvest advisories, thereby improving measurement precision without directly exposing the complexity of the underlying systems to end users.
Solution Approach 2:
The system performs multiple functions within a unified framework: it collects location-tagged weather data, processes user feedback, runs physical and empirical models, generates predictions, and delivers harvest advisories. This multi-functional approach consolidates what would otherwise require separate systems, improving measurement precision across various parameters (crop moisture, soil conditions, weather forecasting) while managing overall device complexity through integration.
2Reliability
If location-tagged data communication and user feedback are integrated into the modeling system, then reliability of harvest predictions is improved, but device complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where user observations and actual harvest outcomes are continuously fed back into the modeling system. This feedback loop allows the system to refine its predictions by comparing expected versus actual results, thereby improving reliability over time. The feedback is integrated into the existing modeling framework rather than requiring a completely separate system, managing complexity through iterative improvement.
Solution Approach 2:
The system merges multiple data sources (location-tagged communications, user feedback, weather station data, satellite imagery) into a unified modeling framework. By combining these diverse inputs through integrated physical, empirical, and AI models, the system improves prediction reliability while avoiding the complexity of managing separate independent systems for each data source.
3Productivity
If comprehensive weather data integration is implemented, then productivity of harvest operations is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary weather simulation and prediction analyses before harvest operations begin. By providing advance harvest advisories that predict optimal harvest windows, crop moisture levels, and potential weather risks, the system enables farmers to plan harvest operations more efficiently. This preliminary action improves productivity by preventing delays and optimizing resource allocation, while the complexity is managed through automated modeling rather than manual analysis.
Solution Approach 2:
The patent replaces manual weather analysis and harvest planning processes with automated computational models. Instead of farmers manually analyzing weather data and making harvest decisions, the system uses physical, empirical, and AI models to automatically generate predictions and advisories. This substitution improves productivity by providing faster, more accurate analyses while managing complexity through software automation rather than human effort.
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 analyzes.