Aquasafe: A Remote Sensing, Web-Based Platform for the Support of Precision Fish Farming

Abstract

Marine aquaculture has been expanding rapidly in recent years, driven by the growing demand for marine products. However, this expansion has led to increased competition for space and resources with other coastal zone activities, which has resulted in the need for larger facilities and the relocation of operations to offshore areas. Moreover, the complex environment and exposure to environmental conditions and external threats further complicate the sustainable development of the sector. To address these challenges, new and innovative technologies are needed, such as the incorporation of remote sensing and in-situ data for comprehensive and continuous monitoring of aquaculture facilities. This study aims to create an integrated monitoring and decision support system utilizing both satellite and in-situ data to monitor aquaculture facilities on various scales, providing information on water quality, fish growth, and warning signs to alert managers and producers of potential hazards. This study focuses on identifying and estimating parameters that affect aquaculture processes, establishing indicators that can act as warning signs, and evaluating the system’s performance in real-life scenarios. The resulting monitoring tool, called “Aquasafe”, was evaluated for its effectiveness and performance by test users through real-life scenarios. The results of the implemented models showed high accuracy, with an R2 value of 0.67. Additionally, users were generally satisfied with the usefulness of the tool, suggesting that it holds promise for efficient management and decision making in marine aquaculture.

Premises for a digital twin of the Atlantic salmon in its world: Agency, robustness, subjectivity and prediction

“…To improve aquaculture productivity and reduce unwanted mortality and environmental impacts, the Atlantic salmon industry strives towards precision fish farming, entailing full operational control based on continuous digital monitoring (Antonucci & Costa, 2020; Bekkozhayeva & Cisar, 2022; Chatziantoniou et al., 2023; Føre et al., 2018; Royer & Pastres, 2023). This effort has led to a snowball of automatically collected data as well as artificial intelligence (AI) tools to aid in their analysis and monitoring (Mustapha et al., 2021).…”

Determination of Crop Soil Quality for Stevia rebaudiana Bertoni Morita II Using a Fuzzy Logic Model and a Wireless Sensor Network

“…Monitoring systems, often referred to as internet of things (IoT) or precision agriculture systems, have emerged as a common solution for collecting data on various variables of interest. These systems enable the gathering of environmental variables [13][14][15][16][17], soil parameters [18][19][20][21][22], and water parameters [23]. Typically, such systems comprise electronic sensors designed to measure specific variables, along with microcontrollers or interconnected microcontrollers that interpret the sensor data and transmit it wirelessly as a wireless sensor network (WSN).…”

“…To facilitate data analysis, these monitoring systems often include a graphical interface that allows users to access and visualize the collected data. Moreover, the interface may support data export for further processing and analysis [13,14,16,17,[19][20][21][22][23].…”