Applications of Artificial Intelligence Methods for Irrigation Water Quality Index: Review
Bibliographic record
Abstract
The irrigation water quality index (IWQI) is utilized to quantify the suitability of water for growing crops.Irrigation water quality is important because it affects soil properties, plant growth, and agricultural production.The IWQI is calculated to reduce complex water quality data to a single number index to make it easier for decision-makers, researchers, and farmers to evaluate whether the water is appropriate for applying irrigation.When determining the IWQI, several parameters must be taken into consideration, including sodium absorption ratio (SAR), electrical conductivity (EC), pH levels, and concentrations of sulfates, chlorides, bicarbonates, and heavy metals that may be toxic to crops.The IWQI is calculated using these parameters, where a higher IWQI score indicates that irrigation is appropriate due to the water quality.This study reviewed previous studies that discussed artificial intelligence (AI) algorithms from 2016 to 2024 to predict the IWQI.Researchers have turned to artificial intelligence techniques to estimate and predict IWQI instead of traditional methods such as linear regression.Traditional methods have limitations, including a reliance on large sample sizes to achieve high accuracy and reduce errors.Also, large sample sizes require laboratory tests that demand more time, effort and cost.Small sample sizes, on the other hand, often result in inaccurate outcomes, making them unreliable.In addition, traditional methods cannot handle missing or non-linear data and lack the ability to learn from new data for improved accuracy.As for artificial intelligence (AI) algorithms, significant amounts of data are collected in real-time using geographic information systems, remote sensing devices, or other automated systems.These data are processed faster, more accurately, and efficiently, and complex patterns and relationships that may need to be clarified using traditional methods are identified.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".