Resumen:
The measurement of flow in a hydraulic weir with a limnime ter is very important to adequately distribute the vital liquid for uses such as irrigation and human consumption. These measurements by in trusive methods are usually costly, time consuming and even with that investment it is sometimes not enough to ensure reliable results. Wa ter level changes are difficult to measure manually because they are not repetitive due to the climatic variability generated in the places where these weirs are located. This work presents a non-invasive image pro cessing and segmentation system, based on artificial vision and IoT, that ensures the detection and measurement of flow in a hydraulic weir over space and time. Tests developed in different environmental conditions during the course of the day, show a more efficient measurement with an error of less than ± 2% and a rate of 6 samples/hour.
