Closed-Loop Scheduling and Control for Precision Irrigation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In agriculture irrigation management, irrigation scheduling is typically performed in an open-loop fashion and is done only once at the beginning of a growing season. In this work, we study whether closed-loop scheduling with closed-loop control can lead to improved performance in terms of crop yield and water conservation in agriculture irrigation. The interaction between soil, water, crop (maize in this work), and atmosphere is described by an agro-hydrological model, which is a partial differential equation. In the proposed scheduling and control scheme, both the scheduler and the controller are designed using model predictive control (MPC). The scheduler uses a long horizon (with a sampling period of 1 day) that covers the entire crop growth season, and the horizon shrinks as time moves. The primary objective of the scheduler is to maximize the crop yield. The controller uses a much shorter prediction horizon and a much finer sampling period. The primary objective of the controller is to track the soil moisture reference calculated by the scheduler. To alleviate the computational complexity of the scheduler and the controller, a linear parameter varying (LPV) model is identified for the scheduler and controller, respectively. The performance of the closed-loop scheduling scheme is evaluated against the traditional open-loop scheduling scheme under different scenarios.
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.
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 | 0.001 | 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 | 0.000 | 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 it