Soil Fertility Prediction Using Spatiotemporal Graph Neural Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Soil health and fertility are essential components for effective farming.The maintenance of soil health is multipurpose.It supports both plant growth and tackles environmental issues like soil erosions.However, modern agricultural activities and the use of chemical fertilizers affect the quality of the soil.To improve the soil health, the timely prediction of soil fertility is needed.In this work, a deep learning model is proposed for accurate soil fertile prediction.The proposed model is based on a Spatiotemporal Graph Neural Network (STGNN) which considers both spatial and temporal properties of the soil for prediction.Further, the parameters of the model are modified using the metaheuristic optimization algorithm of Red Kite Optimizer.The model is trained and validated on a real-world soil dataset sourced from Kaggle, achieving a classification accuracy of 95.86%, an F1-score of 94.72%, and an RMSE of 0.089.Comparative analysis shows our STGNN model outperforms existing ML and DL models, including CNN, LSTM, and Random Forest, by 3.8% to 6.4% in prediction accuracy.This work provides a robust and scalable model for proactive soil management.It is used for data-driven decision-making in precision agriculture and contributes to longterm soil sustainability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.008 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it