Field-Level Crop Dry-Down Forecasting for Harvest Window Decisions

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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

VSEngineering Contradiction Analysis

1Measurement precision

If field-level weather monitoring is implemented through in-situ sensors, then measurement precision of local conditions improves, but device complexity and cost increase significantly

Engineering Contradiction:

Improvefield-level weather condition precisionVSAvoidmonitoring system complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent introduces an intermediary layer between weather data sources and agricultural models - a weather simulation system that uses regional weather data combined with local field characteristics (soil type, topography, crop canopy) to generate hyper-local weather conditions. This mediator translates coarse regional data into fine-scale field conditions without requiring dense sensor networks.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system creates virtual copies of weather conditions through simulation models rather than physical sensing. By copying and adapting regional weather patterns to specific field conditions using agronomic models, the system achieves field-level precision without deploying physical sensors throughout the field.

Inventive Principle:

Principle #26Copying

2Reliability

If comprehensive in-situ monitoring of field conditions is deployed, then reliability of harvest timing decisions improves, but loss of time for setup and maintenance increases

Engineering Contradiction:

Improveharvest timing decision reliabilityVSAvoidtime for monitoring setup and maintenance

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary weather simulation and crop condition assessment before harvest decisions are needed. By continuously running agronomic models that predict crop moisture, soil conditions, and weather impacts in advance, the system prepares harvest recommendations proactively rather than requiring last-minute monitoring setup.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The agronomic models operate autonomously using automated weather data feeds and pre-configured field parameters. The system self-updates crop conditions and generates harvest advisories without requiring manual field measurements or sensor calibration, eliminating the need for farmer intervention in monitoring activities.

Inventive Principle:

Principle #25Self-service

3Adaptability or versatility

If location-tagged data communication is implemented across multiple fields, then adaptability of harvest planning to different locations improves, but loss of information through data management challenges increases

Engineering Contradiction:

Improveharvest planning adaptability to locationsVSAvoiddata management accuracy

Core Design Contradiction:

Adaptability or versatilityVSLoss of information

Solution Approach 1:

The system segments weather and field data by specific location identifiers (field IDs, GPS coordinates) and processes each location independently through its own agronomic model instance. This segmentation ensures that location-specific conditions (soil type, topography, crop variety) are preserved and processed separately, preventing data conflation while enabling multi-field management.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system transforms raw location-tagged data into standardized agronomic parameters (soil moisture capacity, evapotranspiration rates, crop development stage) that are universally applicable across different locations. By converting diverse field data into consistent model parameters, the system maintains data integrity while enabling comparison and planning across multiple fields.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS9140824B1Diagnosis 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

Publication Date: 2015.09.22 DTN LLC

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.