Baiting is an important part of shrimp pond culture, and obtaining the weight of the remaining bait is the key to achieving accurate baiting. In order to solve the problem of inaccurate weighing of the remaining bait caused by the environment, the system itself and other factors, an adaptive strong-tracking unscented Kalman filtering algorithm （SHFL-ASTUKF） for the dynamic weighing of the bait on baiting boats is proposed. Firstly, a second-order model of the weighing system was established and the measurement results were filtered. Then, based on the fast introduction of the asymptotic cancellation factor, the singular value decomposition was used to replace the Cholesky decomposition to deal with the error covariance problem. Meanwhile, the fuzzy control algorithm and Sage-Husa adaptive filtering were combined to adaptively update the measurement noise covariance and the system noise covariance, so as to suppress the divergence in the filtering process. Example data simulation and experimental validation of SHFL-ASTUKF show that, compared with the strong tracking unscented Kalman filter, the RMSE is improved by 14.9% in the example data simulation, and the RMSE is improved by an average of 15.15% in the experiments of the bait weighing of the remaining bait in the baiting boat and the MAE is improved by 17.27%. The proposed algorithm has higher dynamic measurement accuracy and better noise reduction effect.