Harvest Advisory System for Weather-Driven Drying Cost Optimization

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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 inadequate consideration of localized weather and environmental factors, resulting in yield loss and increased costs.

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, plant, and soil conditions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of energy

If in-field drying is used to reduce moisture levels, then drying costs are reduced, but harvest timing flexibility is reduced due to weather dependencies

Engineering Contradiction:

Improvedrying costsVSAvoidharvest timing flexibility

Core Design Contradiction:

Loss of energyVSAdaptability or versatility

Solution Approach 1:

The system performs preliminary assessment of field moisture conditions and weather forecasts before harvest operations begin, allowing farmers to proactively plan whether to use in-field drying or proceed directly to harvest, thus reducing costs while maintaining timing flexibility through advance decision-making

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system continuously monitors field moisture levels and updates predictions based on real-time weather data and actual drying performance, providing feedback that allows dynamic adjustment of harvest timing and drying strategy to optimize both cost reduction and timing flexibility

Inventive Principle:

Principle #23Feedback

2Reliability

If harvest is delayed to achieve lower moisture levels, then storage stability is improved, but yield loss increases due to excessive dryness and seed damage

Engineering Contradiction:

Improvestorage stabilityVSAvoidyield loss

Core Design Contradiction:

ReliabilityVSLoss of substance

Solution Approach 1:

The system establishes preliminary moisture threshold targets for storage stability and predicts the optimal harvest window before it closes, allowing farmers to harvest at the precise moment when storage stability is achieved without excessive drying that would cause seed damage and yield loss

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system dynamically adjusts the target moisture level parameter based on real-time field conditions and weather predictions, optimizing the balance between achieving sufficient dryness for storage stability and preventing excessive dryness that would damage seeds and reduce yield

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If field-level weather monitoring is implemented, then harvest decision accuracy is improved, but system complexity and implementation cost increase

Engineering Contradiction:

Improveweather condition assessment accuracyVSAvoidmonitoring system complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system uses weather forecast data and simplified moisture prediction models as intermediaries between complex atmospheric conditions and practical harvest decisions, translating detailed weather parameters into actionable guidance without requiring farmers to directly monitor or interpret complex weather data themselves

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system uses satellite remote sensing to create a copy or proxy measurement of field moisture conditions, avoiding the need for extensive physical sensor networks in the field while still achieving accurate moisture assessment through remote observation techniques

Inventive Principle:

Principle #26Copying

Data Source

PatentUS9087312B1Modeling of costs associated with in-field and fuel-based drying of an agricultural commodity requiring sufficiently low moisture levels for stable long-term crop storage using field-level analysis and forecasting of weather conditions, grain dry-down model, facility metadata, and observations and user input of harvest condition states

Publication Date: 2015.07.21 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.