Advanced Efficient Feature Selection Integrating Augmented Extreme Learning Machine and Particle Swarm Optimization for Predicting Nitrogen Use Efficiency and Yield in Corn
Bibliographic record
Abstract
Efficient nitrogen management is crucial for improving corn productivity while minimizing environmental impacts. This study evaluates the response of corn to nitrogen fertilization using three key metrics: Yield, Nitrogen Harvest Index (NHI), and Agronomic Nitrogen Use Efficiency (ANUE). The experiment was conducted over three years (2021-2023) across 84 sites in Quebec, Canada, with five nitrogen treatments applied post-emergence (0, 50, 100, 150, 200 kg N/ha) and initial nitrogen applied at seeding (30 to 60 kg/ha). In addition, various soil health indicators, including physical, chemical, and biochemical properties, were monitored to understand their interaction with nitrogen use efficiency. Machine learning techniques, such as Augmented Extreme Learning Machine (AELM) and Particle Swarm Optimization (PSO), were employed to optimize nitrogen recommendations by identifying the most relevant features for predicting yield and nitrogen use efficiency (NUE). The results highlight that integrating soil health indicators such as enzyme activities (β-glucosidase [BG] and N-acetyl-β-D-glucosaminidase [NAG]) and soil proteins into nitrogen management models improves prediction accuracy, leading to enhanced productivity and environmental sustainability. These findings suggest that advanced data-driven approaches can significantly contribute to more precise and sustainable nitrogen fertilization strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".