Mapping Spatial Variability of Sugarcane Foliar Nitrogen, Phosphorus, Potassium and Chlorophyll Concentrations Using Remote Sensing
Bibliographic record
Abstract
Various nutrients are needed during the sugarcane growing season for plant development and productivity. However, traditional methods for assessing nutritional status are often costly and time consuming. This study aimed to determine the level of nitrogen (N), phosphorus (P), potassium (K) and chlorophyll of sugarcane plants using remote sensing. Remotely sensed images were obtained using a MicaSense RedEdge-P camera attached to a drone. Leaf chlorophyll content was measured in the field using an N-Tester chlorophyll meter, and leaf samples were collected and analyzed in the laboratory for N, P and K. The highest correlation between field samples and predictor variables (spectral bands, selected vegetation indices, and plant height from Light Detection and Ranging (LiDAR)), were noted.The spatial distribution of chlorophyll, N, P, and K maps achieved 60%, 75%, 96% and 50% accuracies, respectively. The spectral profiles helped to identify areas with visual differences. Spatial variability of nutrient maps confirmed that moisture presence leads to nitrogen and potassium deficiencies, excess phosphorus, and a reduction in vegetation density (93.82%) and height (2.09 m), compared to green, healthy vegetation (97.64% density and 3.11 m in height). This robust method of assessing foliar nutrients is repeatable for the same sugarcane variety at certain conditions and leads to sustainable agricultural practices in Costa Rica.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".