Research on Precise Feeding Strategies for Large-Scale Marine Aquafarms

Abstract

Breeding in large-scale marine aquafarms faces many challenges in terms of precise feeding, including real-time decisions as to the precise feeding amount, along with disturbances caused by the feeding speed and the moving speed of feeding equipment. Involving many spatiotemporal distributed parameters and variables, an effective predictive model for environment and growth stage perception is yet to obtained, further preventing the development of precise feeding strategies and feeding equipment. Therefore, in this paper, a hierarchical type-2 fuzzy system based on a quasi-Gaussian membership function for fast, precise, on-site feeding decisions is proposed and validated. The designed system consists of two layers of decision subsystems, taking in different sources of data and expert experience in feeding but avoiding the rule explosion issue. Meanwhile, the water quality evaluation is considered as the secondary membership function for type-2 fuzzy sets and used to adjust the parameters of the quasi-Gaussian membership function, decreasing the calculation load in type reduction. The proposed system is validated, and the results indicate that the shape of the primary fuzzy sets is altered with the secondary membership, which influences the defuzzification results accordingly. Meanwhile, the hardware of feeding bins for UAVs with variable-speed coupling control systems with disturbance compensation is improved and validated. The results indicate that the feeding speed can follow the disturbance in the level flying speed.