Assessment of Leaf Phosphorus for Multiple Crop Species Using an Electrical Impedance Spectroscopy Sensor
Bibliographic record
Abstract
Phosphorus is an essential nutrient and plays a critical role in energy reactions in the plant. Deficits of the phosphorus nutrient can influence essentially all energy requiring processes in plant metabolism. Phosphorus stress early in the growing season can restrict crop growth, which can carry through to reduce final crop yield. In this work, the leaf phosphorus levels for multiple crop species like canola, wheat, soybeans, and corn are assessed using an electrical impedance spectroscopy (EIS) sensor in vegetative growth stage. A non-destructive, in-situ, and less complex impedance measurement method is used which is cheaper than other available spectrophotometry, spectral imaging, and optical sensor technologies. EIS sensor is used to develop a binary and multiclass classifier for the assessment of leaf phosphorus based on different machine learning algorithms like K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Bagged Trees. An average accuracy of more than 82% of the models is obtained. A maximum accuracy of 95.4% for Canola and 94.8% for soybeans is obtained using EIS as a binary and multiclass classifier. The precise measurements using a low-cost EIS sensor with high accuracy performed well in the diagnosis of phosphorus deficiencies in multiple crops.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".