Predictive Field Mapping for Real-Time Agricultural Machine Control
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Solution Overview
Problem
Agricultural machines face challenges in optimizing operations due to variability in environmental characteristics across fields, which can affect harvesting efficiency and accuracy, as existing systems lack effective methods to predict and adapt to these variations in real-time.
Innovation Solution
An agricultural system that uses in-situ sensors to detect environmental characteristics while mapping topographic features, generating predictive maps to guide machine control systems for optimized operation, such as yield, crop moisture, and soil moisture prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real-time environmental characteristic detection and prediction systems are implemented, then harvesting efficiency and accuracy are improved, but device complexity increases
Solution Approach 1:
The system performs preliminary actions by generating predictive environmental characteristic maps before harvesting operations begin. The predictive map generator creates spatial distribution maps of environmental characteristics (moisture, temperature, etc.) based on sensor data collected during field traversal, allowing the harvesting system to pre-plan and optimize operations for different field zones.
Solution Approach 2:
The patent introduces an intermediary predictive mapping system that bridges the gap between raw sensor data and harvesting control decisions. The predictive environmental characteristic maps serve as intermediary data structures that translate complex sensor measurements into actionable insights for harvesting optimization, reducing the complexity of direct real-time control.
2Measurement precision
If comprehensive environmental characteristic mapping is performed across the entire field, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system applies partial action by focusing measurements and predictions on critical environmental characteristics that most significantly impact harvesting efficiency. Rather than measuring all possible parameters with equal detail, the predictive map generator concentrates computational resources on key variables such as moisture content and temperature gradients that directly affect harvest quality.
Solution Approach 2:
The patent uses copying by creating simplified predictive representations of environmental characteristics across the field. Instead of performing exhaustive measurements at every location, the system generates predictive maps that copy and extrapolate measured characteristics to unsampled locations based on spatial relationships and environmental models, achieving comprehensive coverage with limited direct measurements.
Data Source
AI summary
An information map is obtained by an agricultural system. The information map maps values of a topographic characteristic to different geographic locations in a field. An in-situ sensor detects values of an environmental characteristic as an agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts the environmental characteristic at different locations in the field based on a relationship between the values of the topographic characteristic and the values of the environmental characteristic detected by the in-situ sensor. The predictive map can be output and used in automated machine control.