UAV-based hyperspectral imaging to evaluate plant moisture and desiccant response in lentil (<i>Lens culinaris</i>)
Bibliographic record
Abstract
Many farmers rely on herbicides as desiccants to dry crops before harvest and aid in harvest efficiency. Optimizing desiccant selection, application rates, and timing is critical for maintaining lentil yield and quality while minimizing herbicide residues. This study utilizes unmanned aerial vehicle-based hyperspectral imaging to assess desiccant efficacy in lentil crops across field trials at Saskatoon (2019, 2020) and Lethbridge (2023) research farms in Canada. Five conventional herbicide treatments, two application rates of an organic desiccant, and an untreated control were evaluated. Desiccation progress was monitored using visual dry-down ratings and plant moisture content at 0, 3-4, 7, 10, 14, and 24 days after treatment. Spectral data were collected using an aerial hyperspectral system and analyzed with partial least squares regression and support vector regression models to predict desiccation response. Year-wise regression analyses yielded R2 values of 47%–68% for visual ratings and 27%–63% for moisture predictions, while combined analyses showed R2 values of 65% and 67%, respectively, with support vector regression consistently outperforming partial least squares regression. The area under the desiccation progress curve for visual ratings, moisture, and normalized difference vegetation index quantified desiccant performance. Ammonium nonanoate (32%) exhibited the fastest desiccation, occasionally surpassing conventional herbicides. These results highlight the potential of unmanned aerial vehicle-based hyperspectral and machine learning for evaluating desiccation efficiency and plant moisture content. This approach advances precision crop management and enhances desiccant screening methodologies in modern agriculture.
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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".