Abstract: To address the insufficient acquisition of key growth data in Litopenaeus vannamei aquaculture, we designed an adjustable intelligent detection system based on a feeding tray, for the acquisition and identification of residual feed and shrimp body length information. The system used a mobile cart as a platform, adjusted the horizontal distance of the feed tray through a retractable cantilever, and employed an ultrasonic sensor combined with a PID control algorithm to achieve precise hovering of the feed tray. The device was equipped with a vision sensor to capture images of the feed tray area, and utilizes image processing algorithms to automatically detect shrimp and residual feed. The results indicate that the success rates of hovering at water depths of 0.5 m (±0.01 m) and 0.05 m (±0.01 m) reached 96.7% and 93.3%, respectively; while relative errors of shrimp body length measurement and residual feed counting were both no more than 6%. This reliable system with precise visual detection offers a viable solution for reducing manual pond inspection intensity and enhancing mechanized and precision feeding in aquaculture.