Harvester Stalk Sensor Data Alignment for Yield Accuracy
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Solution Overview
Problem
Current agricultural systems lack efficient methods for real-time data visualization and analysis of harvest data, particularly in terms of row-by-row yield estimation and alignment of as-planted and as-harvested data, leading to inaccuracies and reduced productivity due to issues like guess row harvesting and GPS drift.
Innovation Solution
A system comprising stalk sensors, processors, and GNSS units integrated with a harvester, capable of sensing, processing, and displaying harvest data in real-time, including features like automatic swath control, data alignment, and suggestion generation for improving yields, which corrects stalk data and provides guidance for accurate harvesting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time data visualization and analysis is implemented, then data accuracy and productivity are improved, but device complexity increases
Solution Approach 1:
The system divides the harvester's operational data into segmented components (stalk sensor data, GNSS position data, row unit data) and processes each segment independently before integrating them for comprehensive analysis. This segmentation enables real-time processing without overwhelming computational complexity.
Solution Approach 2:
A centralized processor acts as an intermediary between the stalk sensors, GNSS units, and display systems, coordinating data flow and processing operations. This intermediary manages the complexity by providing a single point of control for integrating multiple data sources and coordinating real-time visualizations.
2Measurement precision
If row-by-row data alignment is performed, then measurement precision is improved, but processing time increases
Solution Approach 1:
The system performs preliminary alignment operations by establishing reference frames and calibration data during setup phases, so that during actual harvesting operations, the row-by-row alignment can be performed more quickly using pre-established geometric relationships and transformation matrices.
Solution Approach 2:
The system replaces complex mechanical/physical alignment methods with computational geometry and data processing techniques, using mathematical transformations to align rows virtually rather than physically adjusting the harvester, thereby reducing processing time while maintaining precision.
3Productivity
If guess row detection and correction is implemented, then yield loss is reduced, but device complexity increases
Solution Approach 1:
The system continuously monitors stalk sensor data and GNSS position information to detect when the harvester is operating on guess rows (rows that do not correspond to planted seeds). When deviations are detected, the system provides feedback to alert the operator and can automatically adjust harvesting parameters to correct the deviation and prevent yield loss.
4Manufacturing precision
If automatic swath control is implemented, then harvesting accuracy is improved, but device complexity increases
Solution Approach 1:
The automatic swath control system utilizes the existing GNSS units and stalk sensors for multiple functions: positioning, yield monitoring, and swath alignment control. This multi-functionality reduces the need for separate dedicated equipment, thereby limiting the increase in overall device complexity while achieving improved harvesting accuracy.
Data Source
AI summary
An agricultural data system comprising at least one stalk sensor disposed on a harvester configured to sense incoming crop stalks, at least one processor in communication with the at least one stalk sensor, and a display in communication with the at least one processor, wherein the processor is configured to align as-planted data with as-harvested data from the at least one stalk sensor.