Next-Generation Intelligent Prediction Model for Real-Time Soil Nutrients’ Monitoring in Sustainable Farming
Bibliographic record
Abstract
Soil nutrient content (SNC) is a key factor in plant development, and low soil fertility leads to an uneven distribution of essential nutrients such as Nitrogen (N), Phosphorus (P), and Potassium (K). Hence, developing a nutrient predictive model has become an important area of research. Proper nutrient supplementation is essential to enhance crop yield and improve product quality. This paper proposes a model to estimate Soil Nutrient Content (SNC) using Gradient Descent Algorithm-based Multiple Linear Regression (GDA-MLR) algorithm. The model’s performance is evaluated for predicting SNC in agricultural fields. Additionally, Pearson’s correlation analysis is used to examine the relationship between SNC and factors such as moisture percentage (Mp), pH, and other soil nutrients, aiding in feature selection for the model. Model performance is evaluated using Root Mean Square Error (RMSE), Coefficient of Determination (R2), Mean Absolute Error (MAE), and Ratio of Prediction to Deviation (RPD). The proposed GDA-MLR model achieved an R2of 99.97% with urea fertilizer (Nitrogen) application, indicating excellent predictive accuracy. Its low RMSE and high R2demonstrate superior performance compared to existing models. This makes the model well-suited for both precision and sustainable farming, promoting efficient and eco-friendly agricultural practices.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".