An Overview of the Research Status and Advances in Precision Feeding Technology and Equipment in Aquaculture.

Precision feeding is an important foundation for improving production efficiency in aquaculture, reducing feed waste, mitigating water pollution, and promoting the intelligent development of aquaculture. Conventional feeding practices remain heavily dependent on operator experience and are typically executed at predetermined times or fixed ration levels. Such approaches frequently result in extensive feeding management, poor adaptability, low feed utilization efficiency, and delayed responses to environmental changes. Advances in machine vision, the Internet of Things, machine learning, deep learning, and automatic control have progressively shifted aquaculture feeding research beyond standalone automatic feeders toward integrated systems encompassing demand perception, intelligent decision-making, precise control, and equipment coordination. This paper reviews the state of the art in precision feeding technologies and equipment in aquaculture. At the technical level, it summarizes advances in feeding demand perception, intelligent feeding decision-making, and precise control and execution. At the equipment level, it reviews the main types, design features, and field application status of precision feeding equipment in intensive aquaculture, pond aquaculture, and offshore aquaculture scenarios. Despite the considerable progress achieved, the practical deployment of precision feeding still faces several limitations. Environmental disturbances, water turbidity, illumination variation, and sensor drift may compromise the reliability of feeding demand perception. Existing decision-making models frequently exhibit limited generalizability across species, growth stages, and aquaculture scenarios. Moreover, insufficient integration of sensing, decision-making, and execution restricts the development of fully closed-loop feeding systems. High initial investment, maintenance costs, and the shortage of skilled personnel further constrain the adoption of precision feeding equipment, particularly in resource-limited regions. On this basis, the main challenges including sensing accuracy, model practicability, closed-loop control, equipment reliability, and standardization, are examined. Future development trends are also discussed, covering multi-source information fusion, synergy between mechanistic models and data-driven methods, system-level closed-loop control, equipment modularization, and industrial application. This review is expected to provide a reference for subsequent research and engineering applications.