Predicting soil organic matter content in southwestern Ontario fields using imagery from high-resolution digital cameras
Bibliographic record
Abstract
The spatial pattern of soil organic matter (SOM) content may provide information for variable-rate fertilizer nitrogen recommendations, but often requires intensive soil sampling to be properly characterized. This study evaluated whether imagery of bare soils obtained using a high-resolution digital camera system may be used to predict SOM content in southwestern Ontario fields. Using the camera system, image intensity was measured in near-infrared (0.70–0.80 µm) and visible red (0.60–0.70 µm), green (0.50–0.60 µm), and blue (0.40–0.50 µm) wavebands underboth controlled and field conditions for soil samples representative of the range in SOM typically found in southwestern Ontario fields. Under controlled conditions, SOM content did not relate well to image intensity measured in any waveband when multiple soil types were used (r2 ≤ 0.39). Without multiple soil types, image intensity in all wavebands related to SOM content for soil samples taken from two of the sites (r2 ≥ 0.53 for both sites). Image intensity measured under field conditions related to SOM content for only one site (r2 ≥ 0.54 for all wavebands). Variability between SOM content and image intensity shown in this study is most likely attributed to the relative variability in SOM content and confounding factors such as surface residue. Key words: Soil organic matter, soil reflectance, high-resolution digital camera, aerial imagery
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".