A Comparative Analysis of Machine Learning Models for Crop Recommendation in India
Bibliographic record
Abstract
Agriculture serves as the mainstay of India's economy, bearing a vital responsibility in nourishing an expanding populace.The thriving of this sector is contingent upon numerous variables, among which the choice of the optimal crop plays a pivotal role.The advent of Machine Learning (ML) has engendered a transformative impact on the agricultural sector by facilitating the prediction of suitable crops, contingent on soil attributes.This study undertakes the examination of diverse ML algorithms, encompassing Decision Tree, Linear Regression, Naï ve Bayes, Random Forest, Extreme Gradient Boosting (XGBoost), and Support Vector Machine, to assess their efficacy in recommending optimal crops based on soil parameters.The parameters under consideration include Phosphorus, Nitrogen, Potassium, Electrical Conductivity, pH, Organic Carbon, Boron, Iron, Zinc, Copper, Manganese, and Sulphur.The crop recommendations are focused on Rice, Cotton, and Jowar for the Kurnool district of Andhra Pradesh, India.Among the assessed models, it was observed that the XGBoost model surpassed others in terms of accuracy in determining the most suitable crop for the given soil parameters.The experimental findings substantiate the precision of the model in forecasting the apt crop, thereby underscoring the immense potential of ML in the agricultural domain.This investigation signifies a considerable stride towards optimal crop recommendation, thereby increasing the potential for enhanced yield and profitability.Through the incorporation of technological innovation, agriculture can be rendered more efficient, cost-effective, and sustainable, thus laying the groundwork for a promising future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".