Design of an IoT Water Quality Monitoring  System for Tropical Fish Aquaculture

Reduan, Ain; Sani, Maizura Mohd; Kutty, Suhaili Beeran; Isa, Naimah Mat; Yusof, Yusrina

doi:10.6007/ijarbss/v11-i9/10344

Cited by 2 publications

(2 citation statements)

References 4 publications

(6 reference statements)

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“…The limitations of the system are that it does not monitor fish and that the battery should be replaced at regular intervals. Reduan et al [109] used DFRobot Turbidity, DS18B2, and DFRobot PH meters to measure the dissolved oxygen, temperature, and pH levels of water. All these devices were connected to an Arduino board.…”

Section: Iot-based Fish Farm Monitoring Systemsmentioning

confidence: 99%

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Systematic Review on Internet of Things in Smart Livestock Management Systems

Terence,

Immaculate,

Raj

et al. 2024

Sustainability

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The advent of the Internet of Things (IoT) has sparked the creation of numerous improved and new applications across numerous industries. Data collection from remote locations and remote object control are made possible by Internet of Things technology. The IoT has numerous applications in fields such as education, healthcare, agriculture, smart cities, and smart homes. Numerous studies have recently employed IoT technology to automate livestock farm operations. We looked at IoT-based livestock farm management systems in this study. To select the publications for this investigation, we conducted a systematic literature review (SLR) that complied with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. The selected articles were divided into different categories according to their applications. Sensors, actuators, the main controller (gateway), communication protocols, storage, energy consumption, the use of renewable energy sources, scalability, security, and prediction techniques applied to the data collected for future prediction were all examined in this study as IoT technologies used to monitor animals. In this study, we found that only 22% of the articles addressed security concerns, 24% discussed scalability, 16% discussed renewable energy, 18% attempted energy consumption, and 33% employed prediction techniques based on the collected data. The challenges and future directions of intelligent livestock farming are emphasized.

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Section: Iot-based Fish Farm Monitoring Systemsmentioning

confidence: 99%

“…Water Flow and Fish Migration Detection [2] Pollution (Water Characteristics) Monitoring [105][106][107][108][109]111] Farm Automation [110] Table 9. Various aspects of IoT-based fish farm monitoring systems.…”

Section: Application Referencementioning

confidence: 99%