Applying Canny edge detection and Hough transform algorithms to identify irrigation channel boundaries in irrigation districts

【Objective】Airborne technologies have been increasingly used in agricultural sectors for various purposes. In this paper, we developed a fast algorithm for accurately detecting irrigation channel boundaries to support intelligent water resource management in irrigation districts. 【Method】Focusing on the boundary recognition of irrigation channels, unmanned aerial vehicles (UAVs) were used to collect high-resolution images of channels in the Dongshan River (Jurong) and Wuyou Port (Yancheng). The Canny edge detection algorithm was applied to extract edges, followed by the Hough transform to identify and segment channel boundary lines. A novel boundary classification method, based on reference lines and vertical distance metrics, was developed to distinguish channel boundaries. Linear and quadratic polynomial fitting techniques were used to evaluate the accuracy and stability of the extracted channel boundaries. 【Result】Resizing the images to 800 × 400 pixels yielded optimal results, reducing detection time to less than 1.3 seconds while maintaining accurate representation of the channel boundaries. Quadratic polynomial fitting was more accurate than linear fitting in estimating the boundaries. For example, when fitting the straight boundary of the Jurong River, the R2 value was 0.959 2 for the quadratic model and 0.949 2 for the linear model. The quadratic fitting was also more robust in handling boundary noise caused by obstacles. The proposed method can effectively extract channel boundaries, with an R2 greater than 0.99 when using the quadratic fitting method.【Conclusion】Combining traditional Canny edge detection with the Hough transform, along with a grouping method based on reference lines and vertical distances, can rapidly and accurately identify boundaries in multiple channel detections. Our results demonstrate the feasibility of using machine vision techniques and airborne images to effectively and automatically monitor irrigation infrastructures.