Predictive Crop Moisture Maps for Adaptive Harvester Control
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Solution Overview
Problem
Agricultural harvesters face performance degradation when encountering areas with varying crop moisture levels due to differences in plant structure and soil conditions, leading to issues like increased feed rate, plugging, or grain loss.
Innovation Solution
The generation of a functional predictive crop moisture map using in-situ sensor data and prior information maps to adjust machine settings and control the harvester's operation, including the use of vegetative index, topographic, and soil property maps to predict crop moisture levels across a field.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the harvester operates at constant speed through fields with varying crop moisture levels, then productivity is maintained, but performance degradation occurs due to increased feed rate, plugging, and grain loss
Solution Approach 1:
The harvester's operating parameters (speed, feed rate, header height) are dynamically adjusted based on real-time crop moisture predictions from the predictive map, transforming the static constant-speed operation into a dynamic adaptive system that optimizes performance for each field zone
Solution Approach 2:
The predictive map is generated before harvesting by combining prior information maps (soil properties, topography, vegetative index) with in-situ sensor data to predict crop moisture levels across the field, allowing operators to prepare appropriate harvesting parameters in advance for different zones
2Reliability
If the harvester adjusts machine settings frequently to accommodate varying field conditions, then harvesting performance is maintained, but operator workload and control complexity increase
Solution Approach 1:
The system performs self-adjustment by automatically modifying harvesting parameters based on the predictive map data, eliminating the need for continuous manual intervention and allowing the machine to adapt to field conditions autonomously
Solution Approach 2:
In-situ sensors provide real-time feedback on actual crop conditions, which is combined with the predictive map to continuously refine and adjust operating parameters, creating a closed-loop control system that maintains optimal performance
3Measurement precision
If the system collects and processes multiple types of map data (vegetative index, topographic, soil property) to generate predictive maps, then prediction accuracy improves, but device complexity and data processing requirements increase
Solution Approach 1:
Multiple independent data sources (prior information maps, in-situ sensor data, vegetative index, topographic data, soil properties) are merged and integrated into a unified predictive map that comprehensively represents crop moisture conditions across the field
Data Source
AI summary
One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.