Soil pH Prediction Using Deep Learning: An Ensemble Approach
Bibliographic record
Abstract
Accurate prediction of soil potential of Hydrogen (pH) is crucial for optimizing agricultural practices and understanding environmental processes. This study investigates the application of deep learning techniques for predicting soil pH levels using the LUCAS 2018 TOPSOIL dataset, enhanced with textural information. The research aims to provide an effective method for estimating this crucial soil property. The methodology involves comprehensive data preprocessing, including imputation, scaling, and encoding, followed by extensive feature engineering, including the creation of interaction terms, ratios, and logarithmic transformations. Additionally, implementing a custom binning technique based on soil science thresholds helped capture non-linear relationships. Various deep learning architectures, including basic Multi-layer perceptron (MLPs) and Convolutional Neural Networks (CNNs), were explored, where hyperparameter optimization was conducted to improve performance. The study results in an ensemble learning approach, combining the predictions of the best-performing deep neural network with an XGBoost regressor, which demonstrated the best predictive performance. The findings emphasize the potential of deep learning for accurate soil pH prediction, offering valuable insights for precision agriculture and informed soil management strategies.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".