Harvest Advisory System Using Field-Level Weather Simulation
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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 inadequate documentation of weather and environmental factors, resulting in suboptimal crop management and reduced profitability.
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 crops, plants, and soils, and provide enhanced decision-making support.
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 harvest planning accuracy is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments weather data collection and processing into field-level specific measurements, separating local microclimate monitoring from regional weather patterns. This allows accurate harvest planning for each specific field while managing complexity through modular data handling.
Solution Approach 2:
The patent introduces an intermediary layer of field-level weather simulation models that translate complex meteorological data into actionable harvest planning insights. This intermediary processing layer simplifies the interface between raw weather data and decision-making requirements.
2Loss of information
If location-tagged data communication and user feedback are combined to generate harvest advisory outputs, then decision-making support is enhanced, but information processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing and validation of location-tagged data and user feedback as they are collected, preparing them in advance for advisory output generation. This reduces the computational burden and processing time when harvest advisories need to be generated urgently.
Solution Approach 2:
The patent implements continuous feedback loops where user responses to harvest advisories are immediately processed to refine and update predictions. This real-time feedback mechanism enhances decision-making support quality by incorporating latest field observations without requiring complete reprocessing of all data.
3Reliability
If physical, empirical, and artificial intelligence models are used to analyze crops, plants, and soils, then model accuracy is improved, but computational requirements and processing complexity increase
Solution Approach 1:
The patent merges physical models, empirical models, and artificial intelligence models into an integrated modeling framework that analyzes crops, plants, and soils simultaneously. This unified approach improves overall model accuracy by capturing interactions between different factors while managing computational requirements through coordinated processing.
Solution Approach 2:
The system dynamically adjusts model parameters and complexity levels based on available data quality, computational resources, and specific analysis requirements. This allows the integrated model to maintain high accuracy when resources permit while reducing computational burden when constraints exist.
4Productivity
If field-level diagnosis and forecasting of weather conditions is implemented, then harvest operation timing is optimized, but equipment and labor resources required increase
Solution Approach 1:
The patent develops a universal field-level diagnosis and forecasting system that can be applied across multiple fields and crop types using the same core technology platform. This multi-functional approach optimizes harvest operation timing across the entire operation while requiring proportional rather than excessive equipment and labor resources.
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.