Automated detection of phenological transitions for yellow flowering plants such as Brassica oilseeds
Bibliographic record
Abstract
Abstract Monitoring crop phenology is crucial for making site‐specific management decisions for crop protection and nutrition. The prominent yellow bloom associated with canola ( Brassica napus L.) and similar yellow‐flowering plants can provide cues about spatial differences as well as timing of crop input requirements. The objective of this study was to remotely characterize the phenological development of Brassicaceae oilseeds such as canola and carinata ( B. carinata A. Braun) in terms of spectral‐temporal dynamics between vegetation density and yellow flower density. Temporal variation of spectral indices (normalized difference vegetation index [NDVI], normalized difference yellowness index [NDYI], and visible atmospherically resistant index [VARI]) were measured in small plots over the growing season in relation to changes in vegetation density and flower density in winter canola and spring carinata. Phenological change between vegetative and reproductive development could be automatically detected using the difference in the change of the sign of ΔIndex values between VARI and NDYI. An overall accuracy of 85% was obtained when testing the algorithm with Landsat 8 data of canola fields near Olds, AB, Canada. The contrasting behavior between reproductive and vegetation indices across flowering transitions was confirmed for three independent datasets across a range of genetic variation in Brassica oilseeds as well as geographic variation in soil types and management practices. A bivariate time series analysis procedure was developed for automatically estimating flowering transitions based on predictable, relative differences between vegetative and reproductive indices. Researchers and land managers can exploit optimal phenology windows to improve site‐specific models and disease risk assessments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".