## 1 Introduction  
Global aquaculture faces the dual challenges of meeting escalating production demands while ensuring environmental sustainability, with FAO reports indicating record-breaking production levels [1]. The urgent need for technological transformation in aquaculture has become increasingly apparent as traditional farming methods struggle to meet the growing global food security demands while maintaining environmental responsibility. [PERSON] et al. (2021) emphasized in their comprehensive review in _Nature_ that sustainable development necessitates precise management approaches [2]. [PERSON] et al. (2020) analyzed the historical context and future challenges of sustainable aquaculture [3], while [PERSON] et al. (2016) highlighted the limitations of traditional farming methods in terms of management efficiency [4]. This pressing need for enhanced monitoring and control capabilities in aquaculture operations drives the fundamental motivation for developing integrated technological solutions that can bridge the gap between traditional practices and modern precision farming requirements. The conceptual framework of precision aquaculture has emerged from a multidisciplinary integration of various fields. [PERSON] and [PERSON] (2019) were the first to systematically define precision aquaculture in IEEE Internet of Things Magazine [5]. [PERSON] et al. (2018) developed an integrated framework for precision fish farming, published in _Biosystems Engineering_[6]. [PERSON] and [PERSON] (2020) provided a critical review of engineering innovations within the field [7]. [PERSON] et al. (2013) analyzed the role of fish in addressing nutritional needs [8], while [PERSON] et al. (2020) conducted a regional analysis of global aquaculture growth [9]. [PERSON] et al. (2018) examined the opportunities and challenges associated with offshore aquaculture [10]. [PERSON] et al. (2018) published a comprehensive review of intelligent feeding control methods in _Reviews in Aquaculture_[11] and [PERSON] et al. (2020) contributed research on integrated navigation for autonomous underwater vehicles [12]. In the development of sensor technology, [PERSON] et al. (2018) created low-cost sensor systems for monitoring water quality and fish behavior [13]. Additionally, [PERSON] et al. (2017) developed agent-based simulation systems for underwater sensors [14]. [PERSON] et al. (2020) developed an integrated ROV solution for underwater net-cage inspection in fish farms using computer vision, which showcases a specialized remotely operated vehicle (ROV) design for aquaculture applications, although it is limited by traditional communication methods [15]. Foundational research includes [PERSON] et al. 's (2005) pioneering work on underwater acoustic sensor networks, which identified fundamental challenges in underwater communication [16]. [PERSON] et al. (2016) established concepts of edge computing, providing theoretical foundations for distributed processing [17]. [PERSON] et al. (2022) demonstrated advanced identification and quantification techniques for environmental monitoring in aquaculture systems, contributing to the understanding of environmental challenges in marine farming [18]. The critical motivation for this research stems from four significant gaps identified through a systematic analysis of the existing literature that collectively highlight the urgent need for comprehensive technological integration in aquaculture monitoring. Current research primarily focuses on individual technological components [13; 14; 15; 16; 17; 18], lacking a cohesive integration design that encompasses ROV hardware, communication technologies, control interfaces, and cloud services. Most studies remain confined to controlled environments and lack validation from long-term deployments in actual marine aquaculture settings [13]. The absence of multi-platform control capabilities in existing solutions limits operational flexibility and accessibility, particularly for resource-constrained aquaculture operators.