Abstract: Accurate measurement of irrigation water can enhance the water use efficiency of canal systems in smart agriculture. Conventional flow measurement cannot fully meet the large-scale production in recent years, due to the high cost and low efficiency in complex environments. While existing vision techniques have struggled with the illumination variations, they still lack the natural tracers. In this study, a non-contact approach was presented to measure the surface flow velocity in open channels, termed Feature Matching Velocimetry (FMV), using monocular vision. The biodegradable artificial foam was introduced as a high-visibility and eco-friendly tracer in order to overcome the scarcity of discernible features in the concrete-lined irrigation channels. Feature recognizability was significantly enhanced on the water surface. The ORB features were extracted from the consecutive video frames and then matched using the FLANN algorithm. A spatial constraint filtering mechanism was implemented to eliminate the erroneous matches, thus improving matching accuracy. A zonal processing strategy was used to divide the field of view into three regions for independent analysis, in order to mitigate the adverse effects of uneven illumination (e.g., glare, reflections, and shadows). The optimal displacement was estimated for the surface velocity calculation. Gaussian curve fitting was applied to the histogram of the motion distances that accumulated from thousands of matched feature point pairs across multiple frames. The refined displacement was combined with the camera calibration parameters (intrinsic matrix, camera height Z above water) and frame rate. The precise inversion of the surface velocity was realized after the combination. The surface velocity coefficient (using ηs = 0.9 for concrete channels) was then employed to estimate the cross-sectional discharge, according to the measured center-region surface velocity and flow area. Comprehensive field validation was conducted on both rectangular (0.5m wide) and trapezoidal (0.3m base, slope 1:1) concrete-lined channels under diverse conditions: seven flow rates (30-60 L/s), and three illumination scenarios (sunny, cloudy, nighttime with LED supplemental lighting), totaling 42 test conditions. A high-definition camera (60 fps) was used to record the foam-tagged flow. A propeller current meter was provided to measure the ground-truth velocity. The experimental results demonstrate that superior accuracy and robustness were achieved in measuring the flow velocity. There was a mean absolute error (MAE) of 0.026 m/s and a mean relative error (MRE) of 3.79%. The better performance was achieved over the Spatiotemporal Image Velocimetry (STIV) under all test conditions, particularly under challenging illumination (e.g., reducing MRE to 3.00% compared to STIV's 16.09% in rectangular channels under cloudy conditions). The zonal processing strategy effectively balanced the uneven feature point distribution caused by illumination variations. In discharge estimation, the surface velocity coefficient (ηs=0.9) was calculated with excellent agreement with the electromagnetic flowmeter measurements, indicating MRE below 3.6% (3.59% for rectangular and 3.37% for trapezoidal channels) with the maximum relative errors below 7.2%. The FMV model with the artificial foam tracers also offered some advantages: low cost, non-intrusive measurement, strong resistance to illumination interference, suitability for all-weather operation, and ease of deployment using existing surveillance infrastructure. It effectively solved the limitations on the natural textures or specific lighting inherent in the vision-based techniques. This finding can provide a reliable, practical, and cost-effective technical solution to continuous and accurate flow monitoring in the small-to-medium-sized irrigation districts, thus facilitating their transition towards dynamic, precise, and intelligent water management. Future work will focus on the validation in the real-world irrigation canals and extension to more complex flow regimes