Image-Based Rapid Estimation of Frost Damage in Canola (<i>Brassica napus</i> L.)
Bibliographic record
Abstract
Traditional methods of estimating frost damage to crops are labor-intensive and time-consuming. Remote sensing imagery and vegetation indices can be used for condition assessment, however, the utility of using vegetative indices in assessing frost damage specifically is not known. The objective of this study was to estimate the freezing injury using a vegetative index developed from hyperspectral imagery. Three replicates of six 6-leaf stage canola plants were subjected to a temperature of −10 °C for 6hr. The resulting frozen plants were imaged at 6 different thawing times using an imaging spectrophotometer (400-1000 nm). Normalized difference vegetation index (NDVI) and triangular vegetation index (TVI) were calculated. Contrary to expectations, NDVI values from frozen plants increased in the initial thawing treatments (1, 2 and 4hr) and then decreased at 8hr. Whereas, TVI values decreased gradually with increased duration of thawing. Furthermore, when compared to NDVI, TVI clearly differentiated frozen from control plants and within the freezing treatments. The differential modes of response of the indices to post-freezing reflectance changes is the possible reason. These results suggest that with recent advancements in low altitude remote sensing in the areas of spectral, spatial and temporal resolution, early estimation of frost damage is possible.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".