Enzyme-aware soil fertility prediction using dual optimization with improved SCSO
Pavithra Mahesh, Rajkumar Soundrapandiyan · 2026
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Description
Pavithra Mahesh and Rajkumar Soundrapandiyan build a 2026 soil fertility predictor that takes soil enzyme activity seriously. Enzymes drive nutrient cycling and organic matter breakdown, yet many machine learning tools underuse them amid noisy, tangled data. Feature picking and model tuning are usually done apart, which caps accuracy.
Their framework unifies both steps under an improved sand cat swarm optimizer, a search method modeled on hunting cats, with escape moves and cosine-shaped exploration to avoid early traps. Enzyme-linked features were kept in view, and redundant ones filtered out. Gradient boosting reached 98.48 percent accuracy across cross-validation. A crop mapping step then turns fertility predictions into planting advice.
Catalog record
| CREATOR | Pavithra Mahesh, Rajkumar Soundrapandiyan |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 133501 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |