SVM Prediction Model Interface for Plant Contaminates
Bibliographic record
Abstract
In today's time, our nature is fighting against many life-threatening problems which can even threaten the existence of life on the Earth. Pollution is one of the deadliest problems among them. It is caused primarily by means of air, water and land but air pollution is the most severe and dreadful among them. It is caused by introduction of toxic substances like oxides of Sulphur, nitrogen and carbon into the atmosphere making it unfit for living beings. Along with humans, plants have also become a victim to it, and this fact is mostly ignored. A model has been designed to predict the effect of pollution on plants. Image samples of 5 Indian oxygen rich plants namely Ocimum Tenuiflorum, Sansevieria Trifasciata, Chlorophytum Comosum, and Azadirachta Indica have been taken for analysis and various properties like shape, color, corners and texture of the plants were considered from these input RGB images. As a consequence of these properties and the pollution index value, certain calculations have been performed and the results are compared with the threshold values. Based on the range in which the calculated results lie, the plants will be categorized into a category which depicts the severity level of pollution in the environment. After applying the model on the images, a dataset was prepared and SVM classification model has been trained on it which predict with an accuracy of 85%. It has been presented in the form of an interactive user interface to predict the effect of pollution on plants. Plants are an integral part of nature and should not be ignored.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.008 |
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".