Real-Time Visual Detection of Water Leaks in Irrigation Networks Using Deep Learning: A Smart Solution for Precision Agriculture
Bibliographic record
Abstract
Rapid and accurate detection of water leaks is a critical challenge for preserving increasingly scarce water resources and ensuring sustainable irrigation. Traditional monitoring approaches for water networks, often based on manual inspections or localized sensors, face limitations in terms of cost, accuracy, and responsiveness. Recent advances in computer vision and deep learning open new perspectives for automated monitoring and for automating this process. In this work, we propose a water leak detection system based on the YOLOv11 model, designed to be lightweight and fast, capable of visually identifying areas with anomalies such as water flows or infiltrations in real time. The model was trained using real annotated images via Roboflow, covering diverse conditions. It achieves excellent results, with strong performance in accuracy, recall, and detection precision, outperforming several traditional detection methods reported in related studies. These results demonstrate the relevance of the proposed approach for automated and intelligent water management in agricultural contexts.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".