Predictive Soil Mapping for Ground Engaging Machine Control
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Solution Overview
Problem
Agricultural ground engaging machines face challenges in maintaining optimal performance due to soil property variations, leading to issues such as poor tillage quality and suboptimal seed placement, which conventional sensor-based control systems struggle to address effectively due to latency and inaccuracy.
Innovation Solution
A system that generates predictive soil property maps using in-situ and historical data, allowing for proactive control of machine parameters like depth, downforce, and material application to adapt to varying soil conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional sensor-based control systems are used to monitor soil properties, then real-time data is obtained, but latency and inaccuracy prevent effective control adjustments
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing soil property data before the ground engaging tools actually engage the soil. Predictive maps are generated in advance using in-situ sensors and historical data, allowing the control system to pre-adjust machine parameters (depth, downforce, material application) to optimal values before encountering varying soil conditions, thereby eliminating latency-induced errors and ensuring consistent performance.
2Ease of operation
If ground engaging tools maintain fixed parameters during operation, then machine control is simple, but soil property variations cause poor tillage quality and suboptimal seed placement
Solution Approach 1:
The system implements dynamic control by continuously adjusting machine parameters (depth, downforce, material application rates) based on real-time soil property variations detected by in-situ sensors and predictive maps. The control system dynamically modifies operational parameters across different geographic locations in the worksite, allowing the machine to adapt to changing soil conditions while maintaining precise seed placement and tillage quality through automated, data-driven adjustments.
3Use of energy by moving object
If ground engaging tools engage soil with fixed downforce and depth, then energy consumption is reduced, but varying soil conditions lead to inconsistent performance
Solution Approach 1:
The system applies local quality by tailoring machine parameters (depth, downforce, material application) to specific local soil conditions at different geographic locations in the worksite. Using predictive maps generated from in-situ sensor data and historical information, the control system optimizes energy consumption by adjusting downforce and depth to match actual soil properties in each location, ensuring consistent performance while minimizing unnecessary energy expenditure on areas where fixed parameters would be excessive or insufficient.
Data Source
AI summary
One or more information maps are obtained by an agricultural system. The one or more information maps map one or more characteristic values at different geographic locations in a worksite. An in-situ sensor detects a soil property value as a ground engaging machine operates at the worksite. A predictive map generator generates a predictive map that predicts a predictive soil property value at different geographic locations in the worksite based on a relationship between the values in the one or more information maps and the soil property value detected by the in-situ sensor. The predictive map can be output and used in automated ground engaging machine control.