Near-Infrared Hyperspectral Imaging to Differentiate Wheat Classes
Bibliographic record
Abstract
Differentiation of wheat classes is one of the important challenges to the grain industry. Even though some classes of wheat may look similar, their chemical composition and consequently, the end-product quality can vary significantly. Visual differentiation of wheat classes suffers from disadvantages such as inconsistency, low throughput, and labor intensiveness. A near-infrared (NIR) hyperspectral imaging technique was used to develop a classification model to differentiate eight wheat classes grown in western Canada (Canada Western Red Spring (CWRS), Canada Prairie Spring Red (CPSR), Canada Western Extra Strong (CWES), Canada Western Red Winter (CWRW), Canada Prairie Spring White (CPSW), Canada Western Amber Durum (CWAD), Canada Western Soft White Spring (CWSWS) and Canada Western Hard Winter (CWHW)). Wheat bulk samples (11% moisture content wet basis), 50 g each filled in petri dishes, were scanned in the wavelength region of 960 to 1700 nm at 10 nm intervals using a long wavelength InGaAs NIR Camera. Seventy five mean reflectance features were extracted from the scanned images and used for the identification of wheat classes using an artificial neural network (ANN) and a statistical classifier. Classification accuracy was 100% in classifying CPSR, CWES, CWHW, CWRS, CWRW and CWSWS wheat classes and above 97% for the other two wheat classes (CPSW and CWAD) using linear discriminant analysis. Using quadratic discriminant analysis, the classification accuracy was above 97% for all wheat classes. The classification accuracy of ANN models of two different training patterns (60% training: 30% test: 10% validation and 70% training: 20% test: 10% validation) ranged from 68-100% and 61-100%, respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".