Design, development and analysis of an IoT-based framework for monitoring aquaculture farms

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

doi:10.1504/ijmndi.2019.107702

Cited by 2 publications

(5 citation statements)

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“…Water quality sensor nodes in water quality monitoring systems include various resource constraints such as limited processing capabilities, limited energy, limited data storage memory, lack of communication capabilities, and energy limitation [18]. With the lack of real-time values, the accuracy of data is reduced due to the continuous change of water parameters, and hence, farmers are unable to be warned of the changes in water parameters, which makes them fail to make any preventive actions [30,[50][51][52][53]65].…”

Section: Traditional Monitoring Methodsmentioning

confidence: 99%

“…In the literature, different studies integrated WSN with IoT to transfer the collected data from pH, temperature, turbidity and dissolved oxygen sensors to monitor the quality of water in real time [47][48][49][50][51][52]. In some articles, IoT-based real-time water quality monitoring systems were designed and developed using the Zigbee module to transmit the data via WSN [2,4,19,[53][54][55]. Also, blockchain technology was integrated with IoT, WSN, and GIS technologies for real-time water pollution source tracing [56].…”

Section: System Developmentmentioning

confidence: 99%

“…Managing fish farms and aquaculture production is an extremely challenging task due to the sensitivity and vulnerability of these creations to the quality of water. A few factors in a controlled cultivation environment could affect the quality of aquaculture lives such as human interventions, agricultural wastes, and pollution [53]. Poor management of farm management may cause viral diseases in fish and lead to significant losses to aquaculture and the death of fish [7,19,52,53,57].…”

Section: Farms Managementmentioning

confidence: 99%

“…A few factors in a controlled cultivation environment could affect the quality of aquaculture lives such as human interventions, agricultural wastes, and pollution [53]. Poor management of farm management may cause viral diseases in fish and lead to significant losses to aquaculture and the death of fish [7,19,52,53,57]. In most aquaculture farms, agricultural managers have no enough media and techniques to forecast environmental factors, which is making risk management an impossible task [14].…”

Section: Farms Managementmentioning

confidence: 99%

“…These factors are used to assess the water's cleanliness for uses like drinking water and daily needs. Authors in [53] clarified that their future work will focus on the framework's applicability to pond areas between 2 and 5 acres in size as well as its deployment in the western Godavari region. Additionally, efforts are being made to create new ergonomic and cost-effective designs with the assistance of the industry in preparation for the product's commercialization for use by aquaculture farmers.…”

Section: Recommendationsmentioning

confidence: 99%

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IoT-Based Water Monitoring Systems: A Systematic Review

Zulkifli

Garfan

Talal

et al. 2022

Water

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Water quality monitoring plays a significant part in the transition towards intelligent and smart agriculture and provides an easy transition to automated monitoring of crucial components of human daily needs as new technologies are continuously developed and adopted in agricultural and human daily life (water). For the monitoring and management of water quality, this effort, however, requires reliable models with accurate and thorough datasets. Analyzing water quality monitoring models by utilizing sensors that gather water properties during live experiments is possible due to the necessity for precision in modeling. To convey numerous conclusions regarding the concerns, issues, difficulties, and research gaps that have existed throughout the past five years (2018–2022), this review article thoroughly examines the water quality literature. To find trustworthy peer-reviewed publications, several digital databases were searched and examined, including IEEE Xplore®, ScienceDirect, Scopus, and Web of Science. Only 50 articles out of the 946 papers obtained, were used in the study of the water quality monitoring research area. There are more rules for article inclusion in the second stage of the filtration process. Utilizing a real-time data acquisition system, the criteria for inclusion for the second phase of filtration looked at the implementation of water quality monitoring and characterization procedures. Reviews and experimental studies comprised most of the articles, which were divided into three categories. To organize the literature into articles with similar types of experimental conditions, a taxonomy of the three literature was created. Topics for recommendations are also provided to facilitate and speed up the pace of advancement in this field of study. By conducting a thorough analysis of the earlier suggested methodologies, research gaps are made clear. The investigation largely pointed out the problems in the accuracy of the models, the development of data-gathering systems, and the types of data used in the proposed frameworks. Finally, by examining critical topics required for the development of this research area, research directions toward smart water quality are presented.

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