Remote sensing, geographic information system and modeling techniques for wheat area and production estimation.
Bibliographic record
Abstract
The major wheat producing countries in the world are India, China, USA, France, Russia, Canada and Australia. Global demand for wheat is growing at 1 per cent per year. Crop growth and productivity are determined by a large number of factors such as genetic potential of crop cultivar, soil, weather and management variables, which vary significantly across time and space. Early prediction of crop yield is important for planning and taking various policy decisions. Many countries use the conventional techniques of data collection for crop monitoring and yield estimation based on ground - based visits and reports. These methods are subjective, costly and time consuming. Empirical models have been developed using weather data which is also associated with a number of problems. With the launching of satellites, satellite data are being used for crop monitoring and yield prediction. Most studies have revealed a strong correlation between remotely sensed NDVI and crop yield. GPS/sensor based on-the-go yield monitors are being used in the developed world for yield mapping. A critical analysis of the diverse techniques helps in identification of the most accurate and useful ones. An effort has been made for comparative assessment of these techniques the details of which are discussed in the paper.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".