AAFC annual crop inventory
Bibliographic record
Abstract
Understanding the state and trends in agriculture production is essential to combat both short-term and long-term threats to stable and reliable access to food for all, and to ensure a profitable agricultural sector. In 2007, Agriculture and Agri-Food Canada (AAFC) took its first steps towards the development of an operational software system for mapping the crop types of individual fields using satellite observations. Focusing on the Prairie Provinces in 2009 and 2010, a Decision Tree (DT) based methodology was applied using optical (Landsat-5, AWiFS, DMC, SPOT) and radar (Radarsat-2) imagery. For the 2011 growing season and further years, this activity is extended to other provinces in support of a national crop inventory. At present, this approach can consistently deliver a crop inventory that meets the overall target accuracy of at least 85% at a final spatial resolution of 30m. To achieve full operational status, however, further development is required to optimize the data processing chain. Crop maps covering Canada's entire agricultural region are typically delivered eight months following the end of the growing season. To better meet the needs of AAFC and its partners, as well as those of potential new users, map delivery needs to be more timely. Indeed, there is considerable demand for two map products: an estimated within-season inventory (released during the growing season) as well as a final end-of-season inventory (released shortly after the end of the growing season). To this end, Earth Observation Service (EOS) staff is implementing a new and fully automated crop classifier that should significantly reduce production time. In 2012, the lack of affordable optical data forced AAFC to rely mostly on RADARSAT-2 data. This brings new challenges, given a doubling of the number of images as compared to 2011. In the coming years, new EO data (Landsat 8, Radarsat Constellation Mission, Sentinel-2) will have a significant positive impact on the quality of the AAFC crop inventory.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.084 | 0.047 |
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".