Water quality parameter correlation in a controlled aquaculture environment

Belen, Marivic C. De; Cruz, Febus Reidj G.

doi:10.1109/hnicem.2017.8269429

Cited by 9 publications

(6 citation statements)

References 4 publications

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“…In the last decades, several methods have been developed to monitor or inspect aquatic life like coral reefs and algae, this includes using Autonomous Underwater Vehicles (AUV) [2,22], remote sensing system [1,23] and underwater sensors network [24]. A coral monitoring system particularly for the use of autonomous underwater vehicles (AUVs) has been developed [2] by using the ultraviolet LEDs and image analyses.…”

Section: Related Workmentioning

confidence: 99%

A Reliable Architecture for IoT-based Aquatic Monitoring System of Coral Reef and Algae

Jemat

Sameon²,

Ghapar³

et al. 2023

ARASET

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The coral reef population worldwide faces degradation issues due to various human-related factors such as pollution, overfishing, collecting live corals for the aquarium market, warm climate, thermal stress and ocean acidification. These factors lead to an imbalance in the natural ecosystem where the coral reef and algae reside. Coral reefs are susceptible to the changes in their natural ecosystem. This work aims to study the optimum condition for the coral reef and algae to healthy flourish. The main factors that influence the growth and survival of coral reefs and algae are also studied. Thus, this paper presents a proposed monitoring system-based IoT to monitor the coral reef and algae ecosystem. The system also intends to introduce a regulatory mechanism which can regulate the main factors identified to maintain an optimum condition for the coral and algae to prosper. In order to support the intention, a reliable network system and communication are needed to ensure that accurate information can be obtained. The system consists of sensors, microcontrollers, and a web application for monitoring real data of three different parameters most affected the ocean water: pH, temperature and salinity.

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Section: Related Workmentioning

confidence: 99%

A Reliable Architecture for IoT-based Aquatic Monitoring System of Coral Reef and Algae

Jemat

Sameon²,

Ghapar³

et al. 2023

ARASET

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“…improved locally and internationally, most of them are having problems such as lack of constant monitoring of the sensing system with corresponding correcting devices, insufficient information to the aquaponics enthusiast on the status of the plants' growth, unsuitable climate for specific crops that need lower temperature requirements, and destructive methods of measuring plants (De Belen & Cruz, 2017;Galido et al, 2019;Murad, Harun, Mohyar, Sapawi, & Ten, 2017;Nagayo, Mendoza, Vega, Al Izki, & Jamisola, 2017;Tai et al, 2017;Tolentino et al, 2017).…”

Section: Article Infomentioning

confidence: 99%

Yield Evaluation of Brassica rapa, Lactuca sativa, and Brassica integrifolia Using Image Processing in an IoT-Based Aquaponics with Temperature-Controlled Greenhouse

et al. 2020

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The paper introduced the development of a self-sustainable smart aquaponics system in a temperature-controlled greenhouse with a monitoring and automatic correction system using an Android device through the Internet of Things (IoT) and plant growth monitoring system through image processing using Raspberry Pi. The system involves the acquiring of real-time data detected by the light intensity sensor, and air temperature and humidity sensor. It also includes the monitoring of the pH level and temperature of the recirculating water of the system. If the acquired data is not within the threshold range, the correcting devices, namely grow lights, exhaust and inlet fans, evaporative cooler, aerator, and peristaltic buffer device were automatically triggered by the system to correct and achieve its normal status. The internet remote access includes the effective wireless transmission and reception of data reports between the system and an Android unit with the Android application in real-time. The study focused on the evaluation of two experimental setups comparing the plant growth between conventional soil-based farming and the smart aquaponics system using image processing. After data gathering, results showed that the smart aquaponics setup successfully produced a yield better than the conventional farming setup .

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“…Fish farmers face other challenges represented in the regular feeding of fish and operating costs [3], [4] in addition to the loss of fish due to diseases caused by water pollution [5], [6]. These challenges have encouraged researchers to incorporate modern computer and communication technologies into water quality monitoring and control systems when fish environment become unhealthy [7], [8].…”

Section: Introductionmentioning

confidence: 99%

IoT-based smart monitoring and management system for fish farming

Al-Mutairi¹,

Al-Aubidy²

2023

Bulletin EEI

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Fish farming is still controlled and managed in the traditional way where water quality and fish feeding are manually controlled. There is a need to use computer and communication technology in fish farms for remote monitoring and control. This paper deals with the design and implementation of an internet of things (IoT) based system for real-time monitoring, control and management of fish farming. The design of such a system is based on measuring different types of variables and using the information to control fish growth and increase productivity. Each fish pond is a node in a wireless sensor network. The node contains an embedded microcontroller connected to a set of sensors and actuators and a wireless communication module. Two fuzzy controllers are designed to control the water quality in the ponds as well as the environment using five sensors in each pond plus three environmental sensors. Practical results indicate the accuracy of the measurement system compared to the results obtained from commercial devices used on the farm. These results also showed that the proposed approach achieves the best performance of the real-time monitoring and control system in fish ponds.

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Water quality parameter correlation in a controlled aquaculture environment

Cited by 9 publications

References 4 publications

A Reliable Architecture for IoT-based Aquatic Monitoring System of Coral Reef and Algae

A Reliable Architecture for IoT-based Aquatic Monitoring System of Coral Reef and Algae

Yield Evaluation of Brassica rapa, Lactuca sativa, and Brassica integrifolia Using Image Processing in an IoT-Based Aquaponics with Temperature-Controlled Greenhouse

IoT-based smart monitoring and management system for fish farming

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