Baled Plant Material Quality Scoring with Weighted NIR Sampling
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Solution Overview
Problem
Existing methods for evaluating baled plant material face challenges such as unreliable calibration, lengthy laboratory analysis times, non-representative sampling, and inconsistent results due to non-homogeneous bale composition, which are exacerbated by variable exposure times and positions of NIR sensors during baling.
Innovation Solution
A system and method that associates a unique identifier with each bale, incorporating calibration and evaluation information, and uses weighted averaging of subunit properties to assign a weighted average quality value, enhancing accuracy and consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a core sample is taken from one or more bales and sent to a third-party laboratory for testing, then properties such as protein content, fiber content, and moisture content can be determined, but the process requires long time for sample transport, testing, and analysis, and the sample may not be representative of the overall field production
Solution Approach 1:
The system performs preliminary NIR testing and calibration during the baling process itself, rather than waiting for laboratory analysis. The NIR sensor collects data on plant material as it is being baled, and the system pre-processes this data to predict quality properties, providing results in real-time or near-real-time without requiring subsequent laboratory testing
Solution Approach 2:
The patent introduces an intermediary computational model that translates NIR spectral data into quality predictions. This intermediary system (including calibration models, filtering information, and property prediction algorithms) acts as a mediator between the NIR sensor and the final quality assessment, enabling accurate property determination without direct laboratory analysis
2Ease of operation
If an NIR sensor is mounted in the compression chamber to scan the finished bale, then the sensor can evaluate the bale, but the sensor is exposed to plant material for variable amounts of time depending on baling conditions, resulting in non-representative sampling
Solution Approach 1:
The system dynamically adjusts filtering parameters and weighting factors based on baling conditions such as baler speed, crop mass, and exposure time. By changing these parameters in response to varying operating conditions, the system compensates for non-uniform sampling and maintains accurate quality assessments across different baling scenarios
Solution Approach 2:
The system incorporates feedback mechanisms that monitor actual sensor exposure conditions during baling and use this information to adjust the filtering and averaging of NIR data. The feedback loop ensures that the final quality evaluation accounts for variable exposure times and positions, correcting for biases introduced by non-uniform sampling
3Adaptability or versatility
If different calibration models are used by different laboratories, then each lab can perform testing, but the results vary significantly (30-50% variation) affecting the value and end use of plant material
Solution Approach 1:
The system establishes a universal calibration framework that can be deployed across multiple balers and locations while maintaining consistent results. The calibration models are designed to be transferable and adaptable to different operating conditions without requiring location-specific recalibration, enabling standardized quality assessment across diverse environments
Solution Approach 2:
The system dynamically adjusts calibration parameters based on local conditions such as crop type, moisture content, and equipment configuration. By changing these parameters adaptively rather than using fixed laboratory-specific calibrations, the system maintains measurement consistency across different locations and conditions
4Productivity
If the NIR sensor scans only the outer surface area (approximately twenty square millimeters) to a depth of approximately four millimeters, then the sensor can obtain readings, but the results are unreliable due to non-homogeneous particle size and poor representation of overall plant material
Solution Approach 1:
The system divides the bale into multiple segments or zones and collects NIR data from different locations and depths within the bale. By segmenting the sampling approach rather than relying on a single surface measurement, the system captures a more comprehensive representation of the overall plant material quality
Solution Approach 2:
The system applies filtering and averaging algorithms that account for particle size variations and material heterogeneity. By processing the NIR data to compensate for non-homogeneous conditions, the system produces a unified quality assessment that represents the overall bale rather than being skewed by local variations
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 on-site, accurate, and consistent evaluation of bale quality by integrating RFID tags with NIR testing, ensuring representative sampling and reducing variability in property assignments.
Implementation Method 1
light having wavelengths between, e.g., 780 nm and 2500 nm, is emitted by the instrument and at least a portion is reflected by the plant material; received, filtered, and converted to a voltage or current
Implementation Method 2
received, filtered, and converted to a voltage or current
Data Source
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AI summary
A system and method for evaluating individual subunits of material incorporated into a bale and, based thereon, assigning a weighted average quality value to the overall bale. A baler receives, aggregates, compresses, shapes, and secures subunits of a plant material into a bale. An NIR testing system receives and analyzes near-infrared radiation reflected by the plant material, and generates subunit evaluation data reflecting properties of the material in the subunits. A computer receives and combines the subunit evaluation data to produce overall evaluation data reflecting properties of the bale, and assigns the overall evaluation data to the bale. Combining the subunit evaluation data includes assigning weights to the subunit evaluation data and then averaging the weighted subunit property values. Weighting may be based on the amount of time the NIR testing system is exposed to the material in each subunit, and the amount of time may be mechanically determined.