Identifying crop phenology using maize height constructed from multi-sources images
Bibliographic record
Abstract
In agriculture, crop height is an important indicator that is commonly applied for monitoring physiological-related traits such as above-ground biomass and grain yields. Timely and precisely acquiring information on crop height at a regional scale still remains challenging, and its potential effectiveness for identifying crop phenology is under studied. In this work, unmanned aerial vehicle (UAV)-based RGB and multispectral-based images, and maize height were collected at critical growth stages in 2019, 2020, and 2021. Direct method of extracting maize height using d-value in digital surface models (DSM), and indirect methods using linear regressions by RGB-based vegetation indices (VIs), RGB-based texture indices, and multispectral-based VIs were separately applied to extract maize height. The results indicated that the optimal variables for extracting maize height were DSM and RGB-based VIs, and these variables were then used to construct maize height through a multi-linear regression. The multi-indicators, namely, constructed maize height, RGB-based VIs, and multispectral-based VIs were filtered using a single logistic model (SLM) and HANTS, respectively. The heading and tasseling dates of maize were identified using threshold methods and the results were compared with measured ones. The average of RMSE was 6.83 (7.14) days for constructed maize height, 10.19 (12.02) days for RGB-based VIs, and 8.02 (7.92) days for multispectral-based VIs filtered by SLM and HANTS, respectively. In conclusion, the constructed maize height well described maize’s growth stages and can serve as an important complement means for extracting maize phenology compared to traditional remote sensing-formed VIs.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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".