Field-Level Crop Dry-Down Forecasting for Harvest Timing

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

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 integration of weather analysis, which affects crop moisture, temperature, and soil conditions, resulting in reduced crop revenue and logistical challenges.

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 predict optimal harvest times and conditions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional agricultural models are used without real-time weather integration, then model simplicity is maintained, but prediction accuracy of field-level weather conditions and crop moisture deteriorates

Engineering Contradiction:

Improveprediction accuracyVSAvoidmodel complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent combines multiple data sources including real-time weather data, historical weather patterns, soil characteristics, and crop characteristics into a unified predictive model. This integration merges meteorological forecasting with agronomic modeling to achieve accurate field-level predictions of crop moisture and harvest conditions.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The predictive model is designed to handle multiple functions: predicting crop moisture content, determining optimal harvest timing, assessing field accessibility, and evaluating storage conditions. This multi-functional approach allows a single system to address various aspects of harvest decision-making.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Measurement precision

If in-situ monitoring of field conditions is implemented regularly, then measurement accuracy improves, but operational cost and resource requirements deteriorate

Engineering Contradiction:

Improvefield condition monitoring accuracyVSAvoidoperational efficiency

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The system utilizes existing weather monitoring infrastructure and publicly available weather forecast data rather than requiring dedicated in-situ monitoring equipment at every field. The model processes and interprets this external data automatically, reducing the need for manual field measurements and monitoring resources.

Inventive Principle:

Principle #25Self-service

3Stability of the object's composition

If harvest operations are delayed to achieve optimal moisture levels, then crop storage stability improves, but harvest time and equipment utilization deteriorate

Engineering Contradiction:

Improvecrop storage stabilityVSAvoidharvest timing

Core Design Contradiction:

Stability of the object's compositionVSLoss of time

Solution Approach 1:

The system performs preliminary predictions of crop moisture content and field conditions before harvest operations begin. By forecasting future moisture levels and field accessibility, the model enables advance planning of harvest schedules, allowing operators to prepare equipment and logistics in advance of optimal harvest windows.

Inventive Principle:

Principle #10Preliminary action

4Productivity

If equipment is deployed to remote fields with unfavorable conditions, then harvest coverage increases, but operational reliability and crop quality deteriorate

Engineering Contradiction:

Improveharvest coverageVSAvoidharvest operation viability

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system forecasts field conditions including soil moisture and accessibility before harvest operations are scheduled. This preliminary assessment allows operators to identify fields that are likely to become accessible and suitable for harvest, enabling proactive deployment plans that avoid sending equipment to fields with unfavorable conditions.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS10255390B2Prediction of in-field dry-down of a mature small grain, coarse grain, or oilseed crop using field-level analysis and forecasting of weather conditions and crop characteristics including sampled moisture content

Publication Date: 2019.04.09 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.