Harvester Reel Control Using Predictive Field Maps
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Agricultural harvesters face challenges in adapting to varying field characteristics, leading to inconsistent performance of components like the reel, which affects crop harvesting efficiency and quality.
Innovation Solution
The system generates predictive maps using in-situ and prior data to control agricultural machines, adjusting parameters such as reel height, position, and speed based on field characteristics like crop state, height, and moisture, using sensors and models to optimize harvesting operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the reel operates with fixed parameters, then the device complexity is reduced, but the adaptability to varying field characteristics deteriorates
Solution Approach 1:
The system obtains maps of the field (crop type, crop state, crop height) before harvesting and uses them to pre-determine reel parameters for different field locations. This preliminary preparation allows the reel to adapt to varying field characteristics without requiring complex real-time sensing and adjustment mechanisms during harvesting.
Solution Approach 2:
The system creates a digital representation (map) of the field characteristics and uses this copy to control the reel parameters. Instead of directly sensing and reacting to physical field variations during harvesting, the system uses pre-acquired map data as a surrogate, simplifying the control system while maintaining adaptability.
2Productivity
If the reel parameters are adjusted dynamically based on field characteristics, then the harvesting efficiency is improved, but the device complexity increases
Solution Approach 1:
The system pre-determines reel parameters based on field maps before harvesting begins. This allows efficient harvesting by having parameters ready in advance for different field locations, eliminating the need for complex real-time adjustment mechanisms while maintaining high productivity.
Solution Approach 2:
The system uses sensor data during harvesting to update and refine the predictive maps, creating a feedback loop that improves harvesting efficiency. The control system adjusts reel parameters based on this feedback while leveraging the pre-established relationship models to minimize complexity.
3Productivity
If the reel speed is increased to improve productivity, then the harvesting rate increases, but the quality of crop processing deteriorates due to missed crops, wrapping, and shattering
Solution Approach 1:
The system dynamically adjusts the reel speed based on field characteristics obtained from maps and sensor data. By varying the reel speed according to crop type, crop state, and crop height at different field locations, the system maintains optimal processing quality while maximizing overall harvesting productivity, avoiding the trade-off between constant high speed and consistent quality.
Data Source
Figure 1
Figure 2
Figure 3A
AI summary
A predictive map is obtained by an agricultural system. The predictive map maps characteristic values at different geographic locations in a field. A geographic position sensor detects a geographic location of an agricultural harvester at the field. A control system generates a control signal to control a reel subsystem of the agricultural harvester based on the geographic location of the agricultural harvester and the predictive map.