Image-based recognition and processing system for monitoring water levels in an irrigation and drainage channel

Liu, Wen-Cheng; Chung, Chin-Wan; Huang, Wei-Che

doi:10.1007/s10333-023-00935-9

Cited by 3 publications

(4 citation statements)

References 35 publications

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“…Costabile [3] showed an RMSE of 2.7 to 6.9 cm between simulation and sensor values in a border irrigation 2D simulation, but it is difficult to make a simple comparison as the measurements were taken in the field rather than in the channel. Liu [17] applied filtering and binarization processes to CCTV camera images in the irrigation and drainage channel to estimate the water level by obtaining the number of pixels in the vertical direction and compared it with the radar wave level sensor, reporting an RMSE of 0.05 to 0.18 m and MAE of 0.04 to 0.20 m. Though the image processing method and the size of the channel were different, this study showed smaller error indices.…”

Section: Discussionmentioning

confidence: 90%

“…Likewise, measured water levels have been used for irrigation system decision making and estimating the quantity of agricultural water supplies. Nevertheless, their use was limited to largescale main channel measurements as ultrasonic sensors still have disadvantages, such as relatively high costs in installation and maintenance [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, CCTV cameras are relatively cost-effective and use visual information [15][16][17]. Irrigation channel management using CCTV is intuitive, and it is possible to respond immediately when a problem occurs.…”

Section: Introductionmentioning

confidence: 99%

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Application of Closed-Circuit Television Image Segmentation for Irrigation Channel Water Level Measurement

Kim,

Choi

2023

Water

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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.

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Section: Discussionmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

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Application of Closed-Circuit Television Image Segmentation for Irrigation Channel Water Level Measurement

Kim,

Choi

2023

Water

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“…Image visibility refers to the clarity and recognizability of objects or details in an image, which is affected by factors such as uneven illumination, haze and night time. Poor image visibility, such as reflection, shadow or occlusion of the area of interest, will affect subsequent water line recognition [90]. Resolution represents the smallest spatial interval that can be resolved in an image, and lower resolution can result in lost or blurred water level detail.…”

Section: Limitations 421 Limitations Of the Computer-vision-based Met...mentioning

confidence: 99%

A Review of Non-Contact Water Level Measurement Based on Computer Vision and Radar Technology

Wu,

Huang,

Huang

et al. 2023

Water

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As pioneering non-contact water level measurement technologies, both computer vision and radar have effectively addressed challenges posed by traditional water level sensors in terms of maintenance cost, real-time responsiveness, and operational complexity. Moreover, they ensure high-precision measurements in appropriate conditions. These techniques can be seamlessly integrated into unmanned aerial vehicle (UAV) systems, significantly enhancing the spatiotemporal granularity of water level data. However, computer-vision-based water level measurement methods face the core problems of accurately identifying water level lines and elevation calculations, which can lead to measurement errors due to lighting variations and camera position offsets. Although deep learning has received much attention in improving the generation, the effectiveness of the models is limited by the diversity of the datasets. For the radar water level sensor, the hardware structure and signal processing algorithms have to be further improved. In the future, by constructing more comprehensive datasets, developing fast calibration algorithms, and implementing multi-sensor data fusion, it is expected that the robustness, accuracy, and computational efficiency of water level monitoring will be significantly improved, laying a solid foundation for further innovations and developments of hydrological monitoring.

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Deep Learning to Recognize Water Level for Agriculture Reservoir Using CCTV Imagery

Kwon,

Lee

2023

Water Resour Manage

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Image-based recognition and processing system for monitoring water levels in an irrigation and drainage channel

Cited by 3 publications

References 35 publications

Application of Closed-Circuit Television Image Segmentation for Irrigation Channel Water Level Measurement

Application of Closed-Circuit Television Image Segmentation for Irrigation Channel Water Level Measurement

A Review of Non-Contact Water Level Measurement Based on Computer Vision and Radar Technology

Deep Learning to Recognize Water Level for Agriculture Reservoir Using CCTV Imagery

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