Predictive Field Mapping for Harvester Crop Flow Control
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Solution Overview
Problem
Agricultural harvesters face performance degradation when encountering areas of varying vegetation, leading to non-uniform crop flow and underfeeding issues, which can cause damage and impact machine efficiency and grain quality.
Innovation Solution
The use of in-situ sensors and predictive mapping technology to generate maps that predict agricultural characteristics such as crop flow and underfeeding, allowing for real-time adjustments in machine operation to maintain optimal performance across different field conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If the harvester operates through areas of varying vegetation density, then the harvesting operation can cover the entire field, but the performance of the harvester degrades due to increased material in denser vegetation areas
Solution Approach 1:
The system performs preliminary mapping of vegetation characteristics using satellite imagery and aerial photography before harvesting. This advance information allows the harvester to be pre-programmed with vegetation density data, enabling proactive adjustments to operating parameters before entering high-density areas, thus maintaining performance while covering the entire field
Solution Approach 2:
The harvester dynamically adjusts its operating parameters (such as cutting height, reel speed, and rotor speed) based on real-time vegetation density information. The system transitions from static operation to dynamic adaptation, modifying settings continuously as it moves through different vegetation zones to optimize performance across varying field conditions
2Productivity
If the operator manually modifies control of the harvester upon encountering areas of varying vegetation, then the harvester performance can be adjusted, but the response time is delayed and efficiency is reduced
Solution Approach 1:
The system implements automated feedback control by continuously monitoring vegetation density through sensors and comparing it with target parameters. The control system automatically adjusts operating parameters based on this feedback loop, eliminating the need for manual operator intervention and reducing response time while maintaining optimal performance
Solution Approach 2:
The harvester is equipped with automated control systems that self-adjust operating parameters based on sensed vegetation conditions. The machine performs its own monitoring and adjustment functions without requiring external operator input, thereby eliminating delays associated with manual detection and response to changing field conditions
3Quantity of substance
If the harvester encounters denser vegetation areas, then more material is harvested from those areas, but the increased material degrades harvester performance and causes underfeeding issues
Solution Approach 1:
The system changes key operating parameters (cutting height, reel rotation speed, rotor speed, conveyor belt speed) in response to vegetation density variations. By dynamically adjusting these parameters, the harvester maintains reliable and stable operation even when harvesting from high-density vegetation areas, preventing underfeeding and mechanical stress
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.