Application maps in precision agriculture – grassland production management in Poland
Bibliographic record
Abstract
This article discusses the topic of the use of application maps in precision agriculture (PA), particularly in the context of grassland management, which accounts for over 21% of utilised agricultural area (UAA) in Poland. New technological developments in the area of smart agriculture (Precision Agriculture, Agriculture 4.0), in terms of sensor technology and information processing, are creating a wide range of data acquisition opportunities to document biological production processes with both high temporal and spatial resolution. That information can be used to rationalise production processes and reduce trade-offs between different environmental services. The technologies that support this kind of research are analyses using satellite imagery, and map-based applications like the system developed in the GRASSAT project are discussed in detail in this article. The developed application provides farmers with information on events using free data from the Copernicus Programme (Sentinel-1, Sentinel-2, ERA5-Land reanalyses). Remote sensing indices, such as the Normalised Difference Vegetation Index (NDVI), Leaf Area Index (LAI), and fresh biomass production volumes, are calculated to show the condition of the green vegetation in the grassland plots. Meteorological risks, such as field freezing, are also presented. The GRASSAT application is available in both desktop and mobile versions.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".