Use of Remote Sensing Data for Estimation of Aman Rice Yield
Bibliographic record
Abstract
Weather related crop losses have always been a concern for farmers, governments, traders and policy makers for the purpose of balanced food supplies, demands, trade, and distribution of aid to nations in need. This paper discusses the utility of Advanced Very High Resolution Radiometer (AVHRR)-based vegetation health (VH) indices as proxies for modelling inter annual variation in Aman rice (AR) yield in Bangladesh and for early estimation. We compare annual local and hybrid AR yield with VH Indices computed for each week during 1991â2005. A strong correlation was found between AR yield and VH during the period of AR development that occurs during one/two months in advance of harvest (early October to early November). Stepwise principal components regression (PCR) was used to construct a model to estimate yield as a function of critical-period VH indices. The model reduced the yield prediction error variance by 97% and 92% compared with a prediction of average local Aman rice (LAR) and hybrid Aman rice (HAR) yield for each year respectively.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".