Detection of fungal infection in pulses using near infrared (NIR) hyperspectral imaging
Bibliographic record
Abstract
Pulses are a major source of human protein intake nowadays and will continue to be so because of their high protein content. Pulse crops are members of the family Leguminosae. The five major pulse crops grown in Canada are chick peas, green peas, lentils, pinto bean and kidney beans. Over the past 20 years, Canada has emerged as the world’s largest exporter of lentils and one of world’s top five exporters of beans. These contribute more than $2 billion income to the Canadian economy. The major causes of fungal infection in these pulses are Aspergillus flavus and Penicillium commune. Early stages of fungal infections in pulses are not detectable with human eyes. Near infrared (NIR) hyperspectral imaging system is an advanced technique widely used for detection of insect infestation and fungal infection in cereal grains and oil seeds. A typical NIR instrument captures images across the electromagnetic spectrum at evenly spaced wavelengths from 700 to 2500 nm (a system at the University of Manitoba captures images in the 960 nm to 1700 nm range). From the captured images, the spatial relationships for different spectra in the neighborhood can be found allowing more elaborate spectral-spatial methods for a more accurate classification of the images. The primary objective of this study was to assess the feasibility of the NIR hyperspectral system to identify fungal infections in pulses. Hyperspectral images of healthy and fungal infected chick peas, green peas, lentils, pinto bean and kidney beans were acquired and features (statistical and histogram) were used to develop classification models to identify fungal infection caused by Aspergillus flavus and Penicillium commune. Images of healthy and fungal infected kernels were acquired at 2 week intervals (0, 2, 4, 6, 8 and 10 weeks from artificial inoculation). Six-way (healthy vs the five different stages of infection) and two-way (healthy vs every stage of infection) models were developed and classifications were done using linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) classifiers. The LDA classifier identified with 90-94% accuracy while using the six-way model, and with 98-100% accuracy when using the two-way models for all five types of pulses and for both types of fungal infections. The QDA classifier also showed promising results as it identified 85-90% while using the six-way model and 96-100% when using the two-way models. Hence, hyperspectral imaging is a promising and non-destructive method for the rapid detection of fungal infections in pulses, which cannot be detected using human eyes.
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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 | 0.001 | 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".