Assessing Phytotoxicity in Lentils (Lens culinaris) Using Hyperspectral UAV Imagery
Bibliographic record
Abstract
Weed control is of great importance in the successful management of lentil due to its poor competitive ability and short stature. With a lack of effective herbicides and an increase in herbicide resistant weeds; weed control is becoming even more challenging to lentil producers. Field ratings to assess herbicide safety and phytotoxicity in crops can be a tedious and bias associated process. The objective of this research is to determine if phenotyping crop phytotoxicity is possible using UAV imagery. A two-factor randomized complete block design was conducted at two locations in Saskatchewan, Canada in 2019. The factors lentil variety (CDC Greenstar, CDC Maxim, CDC Impala and CDC Improve) and herbicide rates- including the recommended dose and up to ten times the recommended dose of both saflufenacil and metribuzin herbicides. Unmanned aerial vehicle hyperspectral imagery was captured 6, 16 and 23 days after the application of metribuzin in accordance with visual ratings for phytotoxicity. Increasing herbicide dose decreased both field measures of above-ground biomass and plant stand counts. The greatest spectral variation in reflectance was present for metribuzin versus the saflufenacil herbicide. The spectra were noted to differ especially in the green peak, red-edge, and near infrared regions. Further work is being done to analyze imagery data from 2020 to determine if appropriate vegetative indices can be produced to classify different levels of herbicide tolerance. The end goal of this work is to contribute to improving herbicide screening technology with the ability to assess crop phytotoxicity autonomously via computer algorithms. Link to Video Presentation: https://youtu.be/8H7sm-DUkiI
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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".