Abstract: This study aims to realize the real-time monitoring and online control of material status and parameters of equipment during fish primary processing. Taking Tilapia as the research object, a real-time monitoring system was developed using machine vision. Multi-source data and knowledge fusion were used to establish an online control system for the operational parameters. The system was mainly composed of an industrial computer, PLC, industrial camera, servo motor and monitor. The research contents included: 1) Multi-target images of Tilapia were taken online under the different acquisition frames and exposure time of the camera, according to the changes in the real-time conveying speed of the production line. The influencing factors of Tilapia spreading were investigated in the simulation. A field experiment was then carried out to optimize the structure and operational parameters. 2) Local threshold, Remove, Morphological processing, Median Filter (LRMF) image processing were designed to extract ROI of Tilapia images with different sizes under the grid background. An area-weight model of Tilapia was established under high-speed dynamic conditions. Accurate monitoring of Tilapia feeding rate was realized to reduce the random overlap between fish bodies. 3) Fuzzy control was utilized to improve the stability of the feeding rate during Tilapia processing in the production line. 4) A control software was developed to real-time monitor and adjust the feeding rate and key operating parameters, such as the feeding rate, conveyor belt speed, and descaling drum speed. The test results showed that machine vision was feasible to real-time acquire and tailor the feeding rate of Tilapia in the production line. The best spreading of fish was achieved with the average spreading rate of 87% when the height difference between the hoist and conveyor belt was 15 cm, the conveying speed difference was 0.25 m/s, and the horizontal conveying speed was 0.3-0.7 m/s. The range of spreading rate was 1.87%, suitable for the requirements of feeding rate monitoring. The coefficient of determination was 0.9 in the Tilapia area-weight model, and the accuracy rates for the acquisition of feeding rate, the rotation speed of descaling drum, and the conveying speed reached 95.61%, 98.5%, and 98.6%, respectively. More importantly, the response time of the system was less than 1s. In addition, the fluctuation range of feeding rate was reduced by 43.5% after the application of the system, while the descaling drum realized self-regulation at 80-100 r/min, when the average processing speed of the production line was 2 000 kg/h, indicating the high processing performance of production lines. A rule-based closed-loop online regulation of operating parameters was realized for the requirements of real-time monitoring of fish primary processing. The finding can provide promising technical references for the automation control in the production line of freshwater fish primary processing.