HYPERSPECTRAL IMAGE CLASSIFICATION TO DETECT WEED INFESTATIONS AND NITROGEN STATUS IN CORN
Bibliographic record
Abstract
The potential of hyperspectral aerial imagery for the detection of weed infestation and nitrogen fertilization levelin a corn (Zea mays L.) crop was evaluated. A Compact Airborne Spectrographic Imager (CASI) was used to acquirehyperspectral data over a field experiment laid out at the Lods Agronomy Research Centre of Macdonald Campus, McGillUniversity, Qubec, Canada. Corn was grown under four weed management strategies (no weed control, control of grasses,control of broadleaf weeds, and full weed control) factorally combined with nitrogen fertilization rates of 60, 120, and 250 Nkg/ha. The aerial image was acquired at the tasseling stage, which was 66 days after planting. For the classification of remotesensing imagery, various widely used supervised classification algorithms (maximum likelihood, minimum distance,Mahalanobis distance, parallelepiped, and binary coding) and more sophisticated classification approaches (spectral anglemapper and linear spectral unmixing) were investigated. It was difficult to distinguish the combined effect of both weed andnitrogen treatments simultaneously. However, higher classification accuracies were obtained when only one factor, eitherweed or nitrogen treatment, was considered. With different classifiers, depending on the factors considered for theclassification, accuracies ranged from 65.84% to 99.46%. No single classifier was found useful for all the conditions.
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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 | 0.001 |
| 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) | 0.000 | 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 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".