Investigation of <i>Fusarium</i> damage in wheat using hyperspectral imaging: An independent component analysis approach
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Bibliographic record
Abstract
With the continuously growing world population in the 21st century, the agri-food industry is in dire need of adopting rapid, eco-friendly, and reliable technologies to improve the quantity, quality, and safety of agri-food products to fulfill the world's future food needs. Hyperspectral imaging (HSI), a technique to glean a sample's spectral and spatial information, is an emerging non-destructive technique that can characterize the quality parameters of agri-food products such as Fusarium damage. Despite its vast potential, HSI systems suffer from enormous data sizes, requiring high computational time and power. One potential solution to overcome the aforementioned challenge is to reduce the data size by removing redundant information. However, detecting small optimum features from a large dataset is not trivial. To this end, an exploratory novel HSI data reduction and analysis technique was investigated and validated to identify Fusarium damage in wheat kernels. Wheat samples at three moisture contents (19, 27, and 35%, wet basis) and seven infection levels (ranging from 0 to 56 days after infection) were imaged at 256 equally spaced wavelengths from 820 to 1666 nm. Firstly, complete HSI data was utilized to successfully characterize sound and Fusarium-damaged wheat kernels using independent component analysis (ICA) algorithm. Then, a genetic algorithm optimization approach was used to reduce the data to ten wavelengths for ICA-based analysis. This data reduction approach reduced the computation time to approximately 1.31% of the original time taken for analyzing the full HSI data without compromising the performance of the system. This preliminary study suggests that such wavelength tailoring could reduce the complexity and price of the imaging hardware, e.g., the use of inexpensive non-tunable filters, and less expensive computational hardware, thereby enabling fast and affordable real-time exploration and sorting of grains. This study, while exploratory, fosters advancements in HSI data processing and identifies certain limitations that open new avenues for future research.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it