A state-of-the-art novel approach to predict potato crop coefficient (Kc) by integrating advanced machine learning tools
Bibliographic record
Abstract
The crop coefficient ( K c ) is one of the important elements of the actual evapotranspiration estimation. The current study aims to develop a machine learning approach to estimate the crop coefficient of potatoes (Russet Burbank variety) in Prince Edward Island province, one of Canada's most important producers. The study was conducted at drainage-type lysimeters placed in the potato field with three types of soils (sandy loam, loamy sand, and loam). A machine learning approach using XGBoost, optimized with the Chaos Game algorithm (CGO-XGBoost), was employed to predict K c . Three input scenarios (meteorological + soil data, soil-only, meteorological-only) were tested. Three other machine learning techniques, K-nearest neighbor (KNN), Adaptive Boosting (AdaBoost), and Multilayer Perceptron Neural Network (MLP), were used to compare with the newly developed model. Different performance metrics such as correlation coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) were used to compare different model's performance. Results showed that the CGO-XGBoost model outperformed conventional machine learning models. A comparison of different input scenarios revealed that combination 2 (Soil data only) gave the best results. Combination 3 (only meteorological data) performs weakest among input scenarios. The best model (Combination2 + CGO-XGBoost) achieved the highest accuracy (R = 0.9961, RMSE = 0.0185, MAPE = 2.20%), outperforming traditional methods. SHapley Additive exPlanations (SHAP) interpretability analysis indicates that soil moisture exerts the greatest impact on potato K c . Field Capacity (FC) and Minimum temperature rank as the second and third most significant factors. The integration of SHAP values in the proposed solution improves the interpretability of the model, offering valuable insights into the environmental and soil factors affecting K c predictions. The results showed that the proposed model can accurately predict K c , demonstrating its potential to enhance water-use efficiency and support precision irrigation strategies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".