Real-Time Soil Hydration Assessment and Monitoring Model for Smart Agriculture With IoT Integration
Bibliographic record
Abstract
In modern agriculture, effective water management is a major concern, particularly in the areas where unpredictable climates and water scarcity. Conventional irrigation methods often depend on manual observation, which can result in excessive or insufficient irrigation, which has a detrimental effect on crop productivity, soil health, and resource utilization. Real-time, accurate, and scalable methods for tracking Soil Hydration (SH) levels in a variety of agricultural fields are lacking. This paper introduces an IoT-based Soil Hydration Estimation Model (ISHEM) for smart agriculture, utilizing Multiple Linear Regression (MLR) to estimate soil hydration (SH) content in agricultural fields. The performance of proposed model is compared with commonly used Machine Learning (ML) algorithms such as Adaptive Boost (AdaBoost), Random Forest (RF), Extreme Gradient Boost (XGBoost) and Support Vector Machine (SVM) using various performance measure metrics such as the Coefficient of Determination (R2), Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), Ratio between Prediction to Deviation (RPD) and Ratio of Performance to the Inter-Quartile distance (RPIQ). The ISHEM model demonstrate superior performance over existing ML algorithms by achieving lower values in key error metrics (MAE, MSE, and RMSE), as well as higher values in RPD and RPIQ. This developed model enables accurate estimation of Soil Hydration (SH) content, which can play a crucial role in enhancing the efficiency and effectiveness of smart agriculture practices.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".