Simultaneous multi-crop land suitability prediction from remote sensing data using semi-supervised learning
Bibliographic record
Abstract
Abstract This study presents an artificial neural network based model that can simultaneously estimate land suitability for barley, peas, spring wheat, canola, oats, and soy in Canada leading to more accurate predictions than single-crop models. The novelties in the modelling method include using an indicator function which allows for a multivariate model to be trained, and a semi-supervised learning approach which allows for training with unlabelled data. The model performs well on land not used in the training set, as demonstrated by both K-fold cross-validation and a visual comparison of crop inventory to predicted land suitability in northern Alberta. The predicted suitability of crops correspond with a region's growing season length; this is in line with literature. Northern Canada is almost completely unused for agriculture, but this may change in the coming decades due to the climate becoming more favorable for agriculture. The model presented in this work can allow for a precise cost benefit analysis regarding environmental damage and economic benefits of cultivating new lands in Canada.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 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".