Method and Experiment for Predicting Feed Particle Landing Points in Ship-Mounted Feeding System
[Objective]In order to address the issue of controlling the dispersion of feed particle landing points in cage aquaculture employing ship-mounted feeding systems,a method termed Real-time Feed Particle Trajectory Segmentation and Precise Landing Prediction(MLBP)is proposed.[Method]To address challenges in obtaining parameters inside the feeding tube and at the outlet of feed particle flow,this study employed a high-speed camera to capture images of feed particle trajectories.A proposed hybrid network model was utilized to segment the trajectories and extract key information.For accurate prediction of feed particle landing points,the advantages of a BP neural network were leveraged,with trajectory information and feeding port height serving as inputs to achieve precise predictions.[Result]The MLBP technique,which combined a BP neural network and hybrid network model,reduced single-run execution time by 95%compared to pertinent research techniques.Concurrently,96%of landing points were predicted correctly,with the average error range and percentage down to 32.0%and 30.5%,respectively.[Conclusion]The MLBP method's real-time performance and prediction accuracy are sufficient for cage-feeding operations and offer important information for relevant research.
cage aquacultureship-mounted feeding systemlanding point prediction modelhybrid networkBP neural network
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