Forage Harvester Header Control for Impurity-Aware Crop Intake
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Solution Overview
Problem
Existing self-propelled forage harvesters struggle with the efficient control of headers and working units to minimize the ingestion of contaminants, particularly inorganic impurities, which can lead to contamination of the harvested crop, affecting its quality and causing financial losses.
Innovation Solution
A self-propelled forage harvester equipped with a camera system, image evaluation device, and driver assistance system using machine learning algorithms to analyze crop flow images, autonomously adjusting header and working unit settings to minimize contamination by inorganic impurities, including adjustments to distance, pressure, and drive speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the header is lowered closer to the ground to improve crop intake efficiency, then the productivity increases, but the contamination by inorganic impurities increases
Solution Approach 1:
The system uses cameras to capture images of the crop flow, an image evaluation device with machine learning algorithms to analyze the images and determine the degree of contamination, and a computing unit that automatically adjusts header settings based on this feedback. This closed-loop control enables the system to dynamically optimize the header height and working unit settings to minimize contamination while maintaining productivity.
Solution Approach 2:
The system automatically changes operational parameters (header height, working unit settings) based on real-time contamination analysis. The computing unit adjusts these parameters dynamically in response to the evaluated degree of contamination, allowing the system to adapt to varying field conditions and minimize impurity intake.
2Manufacturing precision
If manual monitoring and adjustment of header settings is performed to reduce contamination, then the purity of harvested crop improves, but the productivity and operational efficiency decrease
Solution Approach 1:
The system performs self-monitoring and self-adjustment of header and working unit settings. The image evaluation device continuously analyzes crop flow contamination, and the computing unit automatically modifies operational parameters without requiring manual intervention. This automation maintains high purity standards while preserving operational efficiency.
Solution Approach 2:
The system replaces manual mechanical adjustment with an automated electronic control system. Cameras, image processing algorithms, and automated actuators substitute for manual monitoring and adjustment, enabling continuous precision control of header settings without reducing productivity.
3Measurement precision
If automated image analysis with machine learning is implemented to detect impurities, then the measurement precision of contamination detection improves, but the device complexity increases
Solution Approach 1:
The image evaluation device with machine learning algorithms serves multiple functions: capturing crop flow images, analyzing contamination levels, identifying different types of impurities, and providing data for automated control decisions. This multi-functional approach achieves high measurement precision while consolidating what could otherwise be multiple separate systems.
Data Source
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AI summary
The present invention relates to a self-propelled forage harvester (1) comprising a height-adjustable header (4) for receiving crop (2), working units (20) for processing the received crop (2), an unloading device (15) for discharging the processed crop (2), a camera system (16) for capturing images (41) of a crop flow (21) passing through the forage harvester (1), an image evaluation device (27) for evaluating the images (41), and a driver assistance system (17) for controlling the header (4), the working units (20), and the unloading device (15), wherein the driver assistance system (17) has a memory (40) for storing data and a computing device (39) for processing the data stored in the memory (40), wherein the image evaluation device (27) together with the driver assistance system (17),The front attachment (4) and the working units (20) form an automatic setting system, in that the image evaluation device (27) is configured to continuously analyze the images (41) of the crop flow (21) for the proportion of, in particular inorganic, impurities contained in the crop flow (21) by means of a machine learning algorithm and to transmit a derived degree of contamination (DC) to the driver assistance system (17), which autonomously and continuously adapts a setting of the front attachment (4) and/or at least one of the working units (20) depending on the degree of contamination (DC).