Resumen
Measuring water levels in an irrigation channel is an important task in irrigation system decision making and estimating the quantity of irrigation water supplies. This study aimed to measure water levels with image information from an irrigation channel. Images were obtained from a CCTV (closed-circuit television) camera and manually annotated to create ground-truth mask images. A comparative analysis was performed using four backbone models (ResNet-18, ResNet-50, VGGNet-16, and VGGNet-19) and two segmentation models (U-Net and Link-Net). ROIs (Regions of Interest), mostly related to the water levels, were selected for converting water pixels to water levels. The U-Net with ResNet-50 backbone model outperformed other combinations in terms of the F1 score and robustness, and selecting an ROI and using a quadratic line between water pixels and water levels showed an R2 of 0.99, MAE (Mean Absolute Error) of 0.01 m, and ME (Maximum Error) of 0.05 m. The F1 score of 313 test datasets was 0.99, indicating that the water surface was sufficiently segmented and the water level measurement errors were within the irrigation system?s acceptable range. Although this methodology requires initial work to build the datasets and the model, it enables an accurate and low-cost water level measurement.