Deep Neural Network System Using Ontology to Recommend Organic Fertilizers for a Sustainable Agriculture
Bibliographic record
Abstract
The highest challenge humankind is facing in the current time period is the enormous population growth and the need to meet the food and nutrient to the population.To meet the enormous production, the farmers are more relayed on the usage of chemicals to increase the food production during the cultivation process.Inclination towards chemical fertilizers is because of their popularity and availability, the over usage of these chemicals is a root cause of many major problems like nutrient-less crops, soil quality degradation, and environmental hazards in the long run.The availability of a knowledge base of the soil quality parameters and their related organic fertilizers according to the farmer's region can decrease the inclination towards the utilization of chemical fertilizers and adopt the usage of organic fertilizers.To help in this process of a major change in farming we built an ontology oriented deep learning model which recommends the farmers in choosing the best organic fertilizers based on the soil quality.The domain ontology construction for agriculture is based on semantic language which can be reused in the future.The knowledge base is then utilized by the deep learning model to process the data and recommend the best suitable fertilizers.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".