Early Nutrient Diagnosis of Kentucky Bluegrass Combining Machine Learning and Compositional Methods
Bibliographic record
Abstract
Kentucky bluegrass (Poa pratensis L.) is the most common perennial turfgrass species grown on playgrounds, municipal and residential lawn areas, and golf tees, fairways and roughs. Fertilization is the most efficient way to improve and maintain turfgrass aesthetic quality. Tissue diagnosis can guide fertilization, but tissue concentration ranges are biased by not taking into consideration nutrient inter-relationships, carryover effects and other key features. The centered log-ratio transformation reflects nutrient interactions in plants and avoids statistical biases. Machine learning (ML) models relate the target variable to the key features ex ante, and can predict future events from prior knowledge. The objective of his study was to predict turfgrass quality from key features and rank nutrients in the order of their limitations. The experimental setup comprised four N, three P, and four K rates applied on permanent plots during three consecutive years. Soils were a loam and an USGA sand. Eleven elements (N, S, P, K, Ca, Mg, B, Cu, Zn, Mn, Fe) were quantified in clippings collected during spring, summer and autumn every year. Turfgrass quality was categorized as target variable by color rating. Concentrations were centered log-ratioed (clr) partitioned into four quadrants in the confusion matrix generated by the xgboost ML model. The area under curve (AUC) and model accuracy were high to predict turfgrass color from the nutrient analyses of clippings collected in the preceding season, facilitating the seasonal adjustment of the fertilization regime to sustain high turfgrass quality. We provide a computational example to run the ML model and rank nutrients in the order of their limitations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.001 |
| 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 teacher head, 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".