Predictive Ground Engagement Mapping for Planting Depth Control

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Solution Overview

Problem

Agricultural planting machines face challenges in maintaining optimal ground engagement due to varying field characteristics such as topography, soil moisture, soil type, and compaction, leading to suboptimal seed placement and potential seed loss, exacerbated by sensor latency issues in controlling ground engagement performance.

Innovation Solution

A system that generates a predictive ground engagement map using in-situ sensors and historical or predicted data maps to control suspension, downforce, and depth subsystems of agricultural planting machines, proactively adjusting to field conditions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional sensor-based ground engagement control is used, then ground engagement can be monitored, but sensor latency causes delayed response to field condition changes

Engineering Contradiction:

Improveground engagement control reliabilityVSAvoidsensor latency

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary action by predicting future ground engagement characteristics based on current sensor data and historical field information maps before actual ground engagement changes occur. The predictive model anticipates variations in downforce margin, ride quality, and percent ground contact based on upcoming field conditions, allowing the control system to proactively adjust rather than reactively respond to changes.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system segments the field into multiple geographic locations with distinct characteristics by using information maps that divide the field into zones with different soil types, topography, moisture levels, and compaction properties. This segmentation allows the predictive model to tailor ground engagement predictions to specific field zones rather than applying a single control parameter across the entire field.

Inventive Principle:

Principle #1Segmentation

2Manufacturing precision

If uniform ground engagement is maintained across the field, then seed placement consistency is improved, but field variations in topography, soil moisture, soil type, and compaction cause suboptimal performance in specific locations

Engineering Contradiction:

Improveseed placement precisionVSAvoidadaptation to field variations

Core Design Contradiction:

Manufacturing precisionVSAdaptability or versatility

Solution Approach 1:

The system applies local quality by adjusting ground engagement parameters specifically for each geographic location based on local field characteristics. The predictive model generates location-specific predictions for downforce margin, ride quality, and percent ground contact based on the information maps that capture local variations in soil type, topography, moisture, and compaction. This allows optimal seed placement precision to be achieved in each local zone rather than using a uniform approach across the entire field.

Inventive Principle:

Principle #3Local quality

3Device complexity

If reactive control based on current sensor data is used, then system complexity is reduced, but seed loss occurs due to delayed response to changing field conditions

Engineering Contradiction:

Improvecontrol system complexityVSAvoidseed loss

Core Design Contradiction:

Device complexityVSLoss of substance

Solution Approach 1:

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback by continuously monitoring current ground engagement characteristics through sensors and comparing them with predictive model outputs. The control system uses this feedback loop to adjust ground engagement parameters in real-time, ensuring seeds are properly placed even when field conditions change during operation.

Inventive Principle:

Principle #23Feedback

Data Source

PatentEP4256935B1Agricultural planting system and method of generating a functional predictive ground engagement map

Publication Date: 2026.04.01 DEERE & CO

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AI summary

An agricultural planting system (300) is disclosed. The agricultural planting system (300) comprising: a communication system (306) that receives an information map (358) that includes values of a characteristic corresponding to different geographic locations in a field; an in-situ sensor (308, 180) that detects a value of a ground engagement characteristic corresponding to a geographic location in the field; a predictive model generator (310, 441) that generates a predictive ground engagement model (311, 450) that models a relationship between the characteristic values and values of the ground engagement characteristic based on the value of the ground engagement characteristic detected by the in-situ sensor at the geographic location and a value of the characteristic in the information map (358) at the geographic location; and a predictive map generator (312) that generates a functional predictive ground engagement map (263, 460) of the worksite, that maps predictive values of the ground engagement characteristic to the different geographic locations in the worksite, based on the values of the characteristic in the information map (358) and based on the predictive ground engagement model. Furthermore, a computer implemented method of generating a functional predictive ground engagement map is disclosed.