NIR Calibration Model Database for Variable Crop Properties
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Solution Overview
Problem
Existing NIR sensor systems for agricultural work machines face challenges in creating calibration models that are not flexibly adaptable to changing substance properties and conditions, leading to inconsistent analysis quality and difficulty in comparing results across different times and crop types.
Innovation Solution
A database structure system that generates and transmits user-specific calibration models for NIR sensors, allowing for adaptation to changing conditions and enabling precise measurement of substance properties, by using raw data from NIR sensors and existing calibration models to create optimized models for specific applications and crop types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing NIR sensor systems use fixed calibration models, then device complexity is reduced, but adaptability to changing substance properties and conditions deteriorates
Solution Approach 1:
The system transitions from static calibration models to dynamic calibration models that automatically adapt to changing substance properties and measurement conditions. The calibration models are continuously updated based on reference measurements and environmental parameters, enabling the system to maintain high measurement precision across varying agricultural conditions without requiring manual recalibration for each scenario.
Solution Approach 2:
The NIR sensor system performs self-calibration by automatically comparing sensor measurements with reference values from laboratory analyses. The system autonomously identifies when recalibration is needed and updates its calibration models without requiring manual intervention, thereby improving adaptability while managing complexity through automated processes.
2Measurement precision
If multiple calibration models are created for different crop types and conditions, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system employs universal calibration models that can handle multiple crop types and measurement conditions through a unified approach. Rather than maintaining separate calibration models for each crop type, the system uses a single calibration framework that automatically adapts to different substances by learning from reference measurements, thereby maintaining high measurement precision across diverse applications without the complexity of managing multiple specialized models.
Solution Approach 2:
The calibration models incorporate dynamic parameter adjustments based on environmental conditions, crop types, and substance properties. By allowing calibration parameters to change automatically in response to measured conditions rather than requiring separate models for each scenario, the system achieves high measurement precision while simplifying model management through parameter-based adaptation.
3Adaptability or versatility
If calibration models are updated frequently to adapt to changing conditions, then adaptability is improved, but loss of time for calibration processes increases
Solution Approach 1:
The system implements periodic calibration updates based on triggers such as time intervals, changes in environmental conditions, or detection of drift in measurement accuracy. Rather than continuous recalibration, the system performs updates only when necessary, maintaining adaptability to changing conditions while minimizing time loss through event-driven calibration scheduling that balances responsiveness with efficiency.
Solution Approach 2:
The system uses feedback from reference measurements and quality control data to automatically determine when recalibration is needed. By monitoring measurement consistency and comparing against reference values, the system triggers calibration updates only when actual degradation in performance is detected, thereby maintaining high adaptability while avoiding unnecessary calibration time expenditure.
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 enables high-quality, flexible analysis of substance properties and conditions, allowing for standardized comparisons and improved analytical precision across different times and crop types, while also allowing users to create and exchange calibration models efficiently.
Implementation Method 1
Near-Infrared (NIR) sensors measure the amount of the light transmitted or reflected by a sample in the near infrared range. Organic substances generally have structure-rich absorption or reflection spectra within this spectral range that arises from the excitation of oscillations of bonds between atoms in these substances.
Data Source
AI summary
Creating NIR sensor calibration models and their use in agricultural work machines is disclosed. A database structure, such as a database structure system, for creating calibration models for an NIR sensor system is used. The database structure includes raw data of the NIR spectra of one or both of plant material and other substances. The raw data are generated by one or more NIR sensor systems assigned to an agricultural work machine. The one or more NIR sensor systems transmit the raw data via an interface for the data traffic with at least one data processing unit external to the agricultural work machine. The database structure comprises one or more calibration models and the raw data, generates user-specific calibration models by using the saved raw data and/or calibration models, and provide user-specific calibration models to a user.