Design and wireless sensor Network Analysis of Water Quality Monitoring System for Aquaculture

Shareef, Zeenat; Reddy, S. R. N.

doi:10.1109/iccmc.2019.8819844

Cited by 13 publications

(5 citation statements)

References 7 publications

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“…The pH sensor used for this system cannot be in continuous contact with water, so a system with a robot arm was designed. This implementation made it possible to correct the problem above and reduce costs due to handling errors, material resources, and labor savings [23,24]. Obtaining environmental data and transmitting the information wirelessly to the computer is essential.…”

Section: Data Collectionmentioning

confidence: 99%

Artificial Intelligence-Based Aquaculture System for Optimizing the Quality of Water: A Systematic Analysis

Capetillo-Contreras,

Pérez-Reynoso,

Zamora-Antuñano

et al. 2024

JMSE

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The world population is expected to grow to around 9 billion by 2050. The growing need for foods with high protein levels makes aquaculture one of the fastest-growing food industries in the world. Some challenges of fishing production are related to obsolete aquaculture techniques, overexploitation of marine species, and lack of water quality control. This research systematically analyzes aquaculture technologies, such as sensors, artificial intelligence (AI), and image processing. Through the systematic PRISMA process, 753 investigations published from 2012 to 2023 were analyzed based on a search in Scopus and Web of Science. It revealed a significant 70.5% increase in the number of articles published compared to the previous year, indicating a growing interest in this field. The results indicate that current aquaculture technologies are water monitoring sensors, AI methodologies such as K-means, and contour segmentation for computer vision. Also, it is reported that K means technologies offer an efficiency from 95% to 98%. These methods allow decisions based on data patterns and aquaculture insights. Improving aquaculture methodologies will allow adequate management of economic and environmental resources to promote fishing and satisfy nutritional needs.

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Section: Data Collectionmentioning

confidence: 99%

Artificial Intelligence-Based Aquaculture System for Optimizing the Quality of Water: A Systematic Analysis

Capetillo-Contreras,

Pérez-Reynoso,

Zamora-Antuñano

et al. 2024

JMSE

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show abstract

“…Researchers have been enhancing water quality monitoring in farms. [1] Implemented a system featuring pH, dissolved oxygen, and temperature sensors per water layer, accessible through web and mobile devices. While wired data transmission boosts signal reliability, it has limitations in field coverage.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of an IoT Based Water Quality Monitoring Device for Domestic Fish Ponds

Babalola,

Goroti

2024

AJERD

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This study focuses on developing an affordable IoT-based water quality monitoring system for domestic fish ponds. The system aims to enable remote monitoring of critical water parameters, offering real-time data access through mobile or web interfaces. It includes an alert system to notify the pond owners of any significant changes in water quality, allowing swift corrective action. The initiative stems from challenges faced by aquaculture farmers due to insufficient knowledge about water pH levels. Understanding pH's importance, especially within the optimal range of 6.5-9.0 for fish culture, is crucial for success. Tests conducted on the system's performance in detecting various pH levels across different pond environments demonstrated its reliability in identifying low and normal pH levels. However, anomalies were observed in detecting higher pH levels, indicating potential sensitivity limitations that need further investigation for system refinement. While the system excelled in detecting low and normal pH levels accurately, improvements are required for detecting higher pH thresholds to ensure comprehensive monitoring across diverse water conditions. This enhancement is crucial for effective fish pond management and reducing losses for aquaculture farmers.

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“…Studies by Ya'acob et al [16] on monitoring water temperature and pH in freshwater fish farming were integrated with websites, and Blynk did not have a control system involving actuators. These studies have significantly contributed to advancing agricultural applications [17]- [22].…”

Section: Introductionmentioning

confidence: 99%

Implementation system monitoring and control temperature and pH in urban silver catfish hatchery to enhance efficiency and responsiveness based on IoT

Amalia,

Nasution

2024

j. sist. manaj. ind.

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AKA Farm is an urban agriculture-based silver catfish hatchery enterprise in Bogor Regency. AKA Farm has successfully met local demand for silver catfish fry production by utilizing limited space within vacant houses in Cihideung Ilir village. The comprehensive facilities, including electricity, wells, roads, and drainage channels, support the success of this operation. Challenges in the silver catfish hatchery are associated with low efficiency and responsiveness due to the complexity of the production process, resulting in suboptimal harvest outcomes. The primary contribution of this research lies in developing and implementing an innovative IoT-based monitoring and control system to address water quality conditions, as fluctuations in water temperature and pH significantly impact fish metabolism and survival. The main objective of this study is to improve efficiency and responsiveness in the hatchery process, aiming for optimal harvest outcomes. The integrated system utilizes the Blynk application for real-time monitoring and control. Another advantage of the system is its automation; when the temperature and pH are not optimal, the actuators automatically optimize the aquarium conditions according to applicable standards. The actuators control heating lamps and release acidic or basic solutions. The system performs real-time and remote monitoring and control, reducing delays in responding to changes in the aquarium environment ultimately substantially improving the survival and growth of silver catfish. Implications of this research include assisting farmers in saving time and energy while increasing the productivity of silver catfish hatcheries. The study also reinforces the system's ability to create reliable water quality, supporting the well-being of silver catfish and ultimately enhancing performance in urban farming.

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Design and wireless sensor Network Analysis of Water Quality Monitoring System for Aquaculture

Cited by 13 publications

References 7 publications

Artificial Intelligence-Based Aquaculture System for Optimizing the Quality of Water: A Systematic Analysis

Artificial Intelligence-Based Aquaculture System for Optimizing the Quality of Water: A Systematic Analysis

Development of an IoT Based Water Quality Monitoring Device for Domestic Fish Ponds

Implementation system monitoring and control temperature and pH in urban silver catfish hatchery to enhance efficiency and responsiveness based on IoT

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