A Price-Forecast-Based Irrigation Scheduling Optimization Model under the Response of Fruit Quality and Price to Water
Bibliographic record
Abstract
Different from the traditional irrigation optimization model based only on the water production function, in this study, we explored the water–yield–quality–benefit relationship and established a general irrigation scheduling optimization framework. To establish the framework, (1) an artificial neural network coupled with ensemble empirical mode decomposition (EEMD-ANN) is used to decompose the original price time series into several subseries and then forecast each of them; (2) factor analysis and a technique for order of preference by similarity to ideal solution (FA-TOPSIS), as an integrated evaluation method, is used to comprehensively evaluate the fruit quality parameters; and (3) regression analysis is used to simulate water-yield and water-fruit quality relationships. The model is applied to a case study of greenhouse tomato irrigation schedule optimization. The results indicate that EEMD-ANN can improve the accuracy of price forecasting. Jensen and additive models are selected to simulate the relationships of tomato yield and quality with water deficit at various stages. Besides, the model can balance the contradiction between higher yields and better quality, and optimal irrigation scheduling is obtained under different market conditions. Comparison between the developed model and a traditional modeling approach indicates that the former can improve net benefits, fruit quality, and water use efficiency. This model considers the economic mechanism of market price changing with fruit quality. Forecasting and optimization results can provide reliable and useful advices for local farmers on planting and irrigation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".