Forage Harvester Optical Measuring System for Real-Time Structural Percentage
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for determining the structural percentage of non-grain components in forage harvesters are time-consuming and imprecise, often not performed during operation, necessitating a more efficient method to optimize the chopping process and prevent rumen acidosis in ruminants.
Innovation Solution
A forage harvester equipped with an optical measuring system using image recognition algorithms to determine the geometric properties of non-grain components, allowing for real-time prediction of the structural percentage and adjustment of machine parameters to achieve optimal chopping, which includes the use of multi-spectral or hyperspectral cameras and an IR camera to differentiate between grain and non-grain components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional sifting or chemical analysis methods are used to determine structural percentage, then measurement accuracy can be achieved, but the process is time-consuming and cannot be performed during operation
Solution Approach 1:
The patent replaces conventional mechanical sifting or chemical analysis methods with an optical measuring system using cameras and image recognition algorithms. This substitution enables real-time determination of structural percentage during harvesting operations while maintaining measurement accuracy through automated image processing and classification of harvested material components.
Solution Approach 2:
The patent creates optical copies (images) of the harvested material using cameras positioned along the transport path. These image copies are then processed through image recognition algorithms to determine geometric properties and classify components, enabling rapid structural percentage determination without physical manipulation or chemical analysis of the actual material.
2Productivity
If optical measuring system with image recognition is implemented, then real-time determination of structural percentage is enabled, but device complexity increases
Solution Approach 1:
The patent employs a multi-functional optical measuring system that performs multiple tasks: capturing images of harvested material, processing images through recognition algorithms, determining geometric properties of components, classifying grain and non-grain components, and calculating structural percentage. This universal system consolidates what would otherwise require separate devices for each function, managing complexity through integration.
Solution Approach 2:
The system incorporates automated image processing and classification algorithms that operate autonomously without requiring manual intervention. The control assembly automatically processes captured images, identifies material components based on geometric properties, and determines structural percentage in real-time, enabling the system to serve itself and reduce operational complexity.
3Measurement precision
If multi-spectral or hyperspectral cameras are used to differentiate grain and non-grain components, then measurement precision improves, but use of energy and device complexity increase
Solution Approach 1:
The patent applies local quality by using multi-spectral or hyperspectral cameras only at specific locations where differentiation of grain and non-grain components is most critical. The optical measuring system is positioned along the harvested material transport path to capture images at optimal points, enabling precise component identification without requiring complex spectral analysis throughout the entire harvesting system.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables precise and efficient determination of the structural percentage of non-grain components during operation, allowing for real-time adjustments to the chopping process, thereby optimizing the harvested material and reducing the risk of rumen acidosis in ruminants.
Implementation Method 1
the control assembly (9) is configured to determine image regions (12) assigned to a non-grain component (6) in the image data (11) in an image recognition routine
Implementation Method 2
The optical measuring system (10) has a camera (11) for recording image data (11) of the harvested material (4) of the harvested material flow (7)
Implementation Method 3
which includes the use of multi-spectral or hyperspectral cameras and an IR camera to differentiate between grain and non-grain components
Data Source
AI summary
A forage harvester. The forage harvester has a work assembly for harvesting a crop and for processing harvested material of the crop, which includes grain components and non-grain components. In operation, the harvested material is transported in a harvested material flow along a harvested material transport path through the harvesting machine. The forage harvester also has a control assembly that includes an optical measuring system arranged on the harvested material transport path. The optical measuring system has a camera for recording image data of the harvested material of the harvested material flow. The control assembly, using an image recognition routine, determines image regions assigned to a non-grain component in the image data, determines geometric properties of the assigned non-grain components based on the image regions, and determines an indicator of a structural percentage of the harvested material from the geometric properties.