NIR Sensor Calibration Models for Variable Crop Conditions
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Solution Overview
Problem
Existing NIR sensor systems in agricultural machines struggle to adapt flexibly to changing material properties and operating conditions, limiting the precision and comparability of material analysis.
Innovation Solution
A database structure is implemented to create and manage calibration models that can be optimized and adapted for specific types of fruits and environmental conditions, enabling flexible calibration and improved data comparison across different operating times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed calibration models are used in NIR sensor systems, then the system structure remains simple, but the system cannot adapt to changing material properties and operating conditions
Solution Approach 1:
The patent implements dynamic calibration models that can be adapted to changing operating conditions and material properties. The system allows calibration models to be updated and modified based on actual operating conditions, transforming the static calibration approach into a dynamic one that evolves with the operating environment.
Solution Approach 2:
The system enables changes in calibration model parameters based on operating conditions and material properties. By allowing parameter adjustments in the calibration models, the system adapts to different grain types, moisture contents, and operating conditions without requiring complete model redesign.
2Measurement precision
If extensive assumptions are made about sample nature for NIR analysis, then the analysis can be performed, but the accuracy is limited by the quality of these assumptions
Solution Approach 1:
The system enables automatic optimization of calibration models using available measurement data. The evaluation unit automatically adjusts and optimizes calibration models based on the measured spectra and known material properties, reducing the need for extensive manual assumptions about sample nature.
Solution Approach 2:
The system uses feedback from actual measurements to continuously improve calibration model accuracy. By comparing measured spectra with expected values and adjusting the calibration models accordingly, the system reduces reliance on initial assumptions about sample composition and properties.
3Measurement precision
If different evaluation models are used for different grain types, then the analysis quality improves, but the results become difficult to compare
Solution Approach 1:
The patent creates a universal calibration model structure that can handle multiple grain types and operating conditions while maintaining result comparability. The standardized data structure and evaluation framework allow different calibration models to be applied consistently across various grain types, ensuring that results remain comparable despite the diversity of materials analyzed.
4Measurement precision
If NIR sensors precisely analyze specific components of material flow, then the component proportions are determined accurately, but the system requires complex calibration models structured differently for each material type
Solution Approach 1:
The patent segments the calibration model into modular components that can be independently configured for different material types while maintaining a consistent overall structure. This modular approach allows precise analysis of specific components in different material flows without requiring completely different calibration model structures for each application.
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
This solution allows for precise and adaptable material analysis, enabling high-quality NIR measurements and standardized data comparison, while also allowing users to create and exchange calibration models efficiently, improving the range of applications and reducing costs.
Implementation Method 1
NIR sensors measure the amount of light transmitted or reflected by a sample in the near-infrared range. Organic substances generally have structurally rich absorption and reflection spectra in this spectral range, resulting from the excitation of vibrational oscillations of bonds between atoms in these substances.
Data Source
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AI summary
The present invention relates to the creation of NIR sensor calibration models and their use in agricultural machinery (1), wherein a database structure (22) is used for creating calibration models (29) for an NIR sensor system (14), which comprises raw data (13) of the NIR spectra (20) of plant material or other substances, the raw data (13) being generated by one or more NIR sensor systems (14) assigned to an agricultural machinery (1), wherein the NIR sensor systems (14) are configured to transmit the raw data (13) via an interface (6) for data exchange with at least one data processing unit (27) outside the agricultural machinery (1), and the database structure (22) comprises and is configured to include at least one or more calibration models (28) in addition to the raw data (13).to generate user-specific calibration models (29) using the stored raw data (13) and/or calibration models (28) and to make these available to a user (30),