Supervised Ontology Oriented Deep Neural Network to Predict Soil Health
Bibliographic record
Abstract
Soil health plays a vital role in agriculture.A nutrient-rich soil helps in better crop growth and high yield.The agriculture data in India is haphazard and no major effort is seen in maintaining them.Soil chemical property is a basic knowledge to decide on cultivation.Knowledge base to help farmers analyse the soil health by using the chemical properties as the main feature in predicting the health and quality of the soil before the cultivation is a key factor for a better production result.This study drives the idea of building a domain ontology model for soil and also utilizes a neural network in predicting the soil by classifying it as healthy or unhealthy based on six chemical parameters that explain the property of soil.Ontology plays as a knowledge base in storing the properties of the soil which also helps in enabling artificial intelligence concepts on the knowledge to make better decisions.MATLAB deep learning toolbox is used to implement the classification and also TensorFlow's Keras was used to handle the data pre-processing, normalization and also the network architecture to validate the result from the toolbox.MATLAB employs the Scaled Conjugate Gradient algorithm and performs with 92% accuracy in achieving the classification of soil.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".