The application of embedded intelligence in canal automation has significantly advanced real-time water resource management, enhancing the efficiency of irrigation systems by enabling precise monitoring of key parameters such as water depth, velocity of flow, discharge etc. In canal systems, automation reduces the need for manual intervention by utilizing smart technologies to monitor and control water flow. Automated controllers and real-time sensors, such as ultrasonic sensors measure flow parameters. This minimizes the need for on-site operators, allowing for a responsive and demand-driven water distribution that adapts automatically to changes in irrigation needs and environmental condition. In the present article, technology readiness level of real time depth monitoring in open channel flow was simulated and the statistical analysis of the data observed using sensors and conventional methods was performed. To compare the manual data and sensor data of depth value in the channel, statistical parameters like one-way ANOVA and Pearson correlation are determined. The result indicates good agreement between the automated data and conventional method. Also, the work comprehends the use of depth sensors, flow sensors and microcontrollers which is integrated to a module for the identification of depth and flow in an open channel.