Development of Decision Support Framework for Soil Conservation and Profitability Mapping using Drone Imagery
Bibliographic record
Abstract
Soil erosion causes fluctuations in potato ( Solanum tuberosum L) yield, deteriorates soil health, lowers profit margins for growers, and creates adverse environmental impacts. This study focuses on developing a decision support system to improve soil health, increase profitability for growers, and lower environmental risks. Two fields are selected for this research in Prince Edward Island, Canada, during 2020-2021. Thermal and topography surveys are conducted to delineate fertility-based management zones (MZs) for soil sampling of organic matter, potato yield, and sensor data collection. Soil temperature are calibrated and validated before the growing season using handheld gun and drone-based thermal imagery. Data analysis from drone imagery reflects the coincided patterns of thermal and multispectral imagery with crop yield, topographical features, and soil water and topography (SWAT) maps. Higher temperature zones lead to excessive soil erosion that reduces soil surface nutrients, lower NDVI values, and show visual variation in bare soil imagery. Moreover, it is found that low thermal zones produce higher yields as compared to the high thermal zones. As such, the slope has a direct impact on the retention of soil moisture content; the low productivity zones retain the least soil moisture content (13.5%), followed by medium (16.1%) and high productivity zones (18.4%). This proposed decision support system can be a useful tool for mapping soil erosion and crop profitability. The data generated from these tools can be a crucial input for improved quantification of sediment transport in watershed scale models.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".