文献类型：期刊文献

中文题名：现代化海洋牧场智能投喂技术与装备发展综述

英文题名：Recent Advances and Development Trends in Intelligent Feeding Technologies and Equipment for Modern Marine Ranches

作者：Yang, Zhou[1,2]; Du, Wendi[2]; Liu, Haitao[1]; Liu, Qing[2]; Duan, Jieli[2]

机构：[1] School of Mechanical Engineering, Guangdong Ocean University, Zhanjiang, 524003, China; [2] College of Engineering, South China Agricultural University, Guangzhou, 510630, China

年份：2026

卷号：57

期号：19

起止页码：1

外文期刊名：Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery

收录：EI(收录号：20264021602155)

语种：中文

外文关键词：Aquaculture - Behavioral research - Digital twin - Feeding - Machine learning - Offshore oil well production - Pelletizing - Pneumatics - Ships - Underwater acoustics

外文摘要：The escalating demand for precision feeding in offshore aquaculture, where feed typically accounts for approximately 70% of full-lifecycle production costs, stands in direct tension with the constrained sensing fidelity and operational reliability imposed by harsh sea conditions, driving feeding systems toward intelligent transformation. This review delineated the scope of intelligent feeding systems for modern marine ranching and systematically examined technological progress and remaining bottlenecks along the "perception-decision-execution" closed-loop framework. At the perception layer, five core environmental parameters (water temperature, dissolved oxygen, current velocity and direction, turbidity, and meteorological conditions) and their sensing principles were reviewed, followed by non-contact biomass estimation via stereo-vision keypoint regression and three-dimensional reconstruction, and feeding-intensity quantification that fused visual cues (residual pellets, surface splash features, and underwater behavior) with active and passive acoustic monitoring. At the decision layer, mechanistic models, including the thermal-growth-coefficient growth curve and bioenergetic balance equations were systematically compared with dynamic models driven by fuzzy control, machine learning, and deep learning architectures (CNN-GRU and Transformer variants), highlighting the trade-off between interpretability and adaptability. At the control and execution layer, the hierarchical "cloud-edge-device" architecture was examined alongside scenario-matched configurations spanning land-based recirculating systems, nearshore cages, and offshore aquaculture vessels, with detailed comparisons of volumetric versus gravimetric metering, peristaltic, hydraulic, and pneumatic conveying tailored to live, fresh-wet, and extruded pellet feeds, and centrifugal, pneumatic, and hydraulic spreading devices. Finally, the evolutionary pathway from mechanized to automated and ultimately intelligent feeding was traced, and four development trends were identified: multimodal spatiotemporal and semantic alignment of heterogeneous sensors, edge deployment of compressed large language models for explainable decision-making, UAV-swarm-based non-contact feeding supported by shipborne take-off, refueling, and battery-swap stations, and digital-twin-enabled iterative optimization of mechanistic models, providing a reference for the unmanned and intelligent development of modern marine ranching. ? 2026 Chinese Society of Agricultural Machinery. All rights reserved.