Predictive Stalk Diameter Mapping for Harvester Deck Plate Control
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Solution Overview
Problem
Agricultural harvesters face performance degradation when encountering varying stalk diameters in the field, leading to issues like increased material other than grain (MOG) intake and grain loss due to improper deck plate spacing, which is challenging to address with existing control methods.
Innovation Solution
The use of in-situ sensors and predictive mapping technology to generate a functional predictive stalk diameter map, which predicts stalk diameters across a field based on sensed data and historical maps, allowing for real-time adjustment of deck plate positioning and spacing to optimize harvesting performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional harvester control methods are used with fixed deck plate spacing, then the machine structure is simple and easy to operate, but harvesting performance degrades when encountering varying stalk diameters, leading to increased MOG intake and grain loss
Solution Approach 1:
The system performs preliminary mapping of stalk diameter variations across the field before harvesting. This advance knowledge allows the harvester to pre-adjust deck plate spacing for different field zones, preventing performance degradation before it occurs. The map is created using sensors that scan the field and store diameter data for later reference during harvesting operations.
Solution Approach 2:
The deck plate spacing is made dynamically adjustable rather than fixed. The system continuously or periodically adjusts the spacing between deck plates based on real-time or near-real-time stalk diameter predictions from the predictive map. This dynamic adaptation allows the harvester to maintain optimal performance across varying field conditions without requiring complex manual intervention.
2Reliability
If deck plate spacing is manually adjusted to match varying stalk diameters, then harvesting performance improves, but the ease of operation decreases and requires constant operator intervention
Solution Approach 1:
The system implements automated feedback control where sensors continuously monitor actual stalk diameter conditions, compare them against the predictive map, and automatically adjust deck plate spacing accordingly. This closed-loop feedback eliminates the need for constant manual operator intervention while maintaining optimal harvesting performance. The operator simply needs to initiate the automated control system.
Solution Approach 2:
The harvester system performs self-adjustment of deck plate spacing using its own onboard sensors and control mechanisms. The automated system monitors field conditions and independently makes adjustments without requiring external operator input for each change, thereby improving ease of operation while maintaining high harvesting performance.
3Reliability
If deck plate spacing is increased to accommodate larger stalk diameters, then stalk processing improves, but smaller stalks may not be properly harvested, reducing overall productivity
Solution Approach 1:
The system applies local quality control by adjusting deck plate spacing according to the specific stalk diameter requirements of different field zones. Rather than using a uniform spacing setting for the entire field, the system tailors the spacing to match local conditions - wider spacing in areas with larger stalks and narrower spacing where stalks are smaller. This localized adaptation ensures optimal processing quality for each zone while maintaining overall harvesting productivity.
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.