Soil Quality Prediction for Determining Soil Fertility in Bhimtal Block of Uttarakhand (India) Using Machine Learning
Bibliographic record
Abstract
Agriculture plays a vital role in the Indian economy. The growth of agriculture sector is based on the type of gift we have got from the nature. It varies state to state, district to district, taluka to taluka, block to block and even village to village. This study is confined to Bhimtal block of Nainital district. The main purpose of agriculture is growing crops and raising livestock. In order to grow the crops several types of agri-inputs are required, among them fertile lands have the great significance in crop cultivation. As far as fertile land is concerned it solely depends on the quality of the soil in terms of producing the nutrients for the crops. The available nutrients present in soil can be evaluated and measured by soil testing tools. The appropriate quantity of soil nutrients supplied to the soil can also be determined by this tool. The quantity of supplied nutrients is based on soil fertility and crop needs. In this study we have classified different soil features such as OC (Organic Carbon), P (Phosphorus), K (Potassium), Mn (Magnesium) and B (Boron). In order to make meaningful inferences and estimates, machine learning techniques especially ANN network with two activation functions relu and tanh are used in this study. For categorizations and predictions we have used village wise soil test report values. This kind of practice will not only help stakeholders to mitigate the expenditure of continuously supplying fertilizers to soil but it would also be cost effective, less time consuming and more profitable for stakeholders. In this regard data was complied, classified, tabulated, presented, analyzed and it can be seen that relu activation function has ensured higher accuracy over tanh activation function. It is expedient and necessary to mention here that out of the five classified soil nutrient parameters relu activation function has shown better performance in respect of four classified soil nutrient parameters while tanh gave better performance in only one classified soil nutrient parameter.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".