Back to the future: IoT to improve aquaculture : Real-time monitoring and algorithmic prediction of water parameters for aquaculture needs

The freshwater shrimp (Macrobrachium rosenbergii) fisheries sector is of vital significance to the economy of Bangladesh. This sector plays an important role in supplying nutrition, employment creation, poverty alleviation, and overseas currency acquisition. Rapid population growth, environmental pollution, and global warming are reducing the production level of the fisheries (freshwater shrimp) sector. There are various environmental metrics, such as acidity and salinity level of water, turbidity, dissolved oxygen level, and so on, which play a vital role in the effective production of shrimps. It is hence paramount environmental and production parameters be constantly monitored to ensure quality in the production chain. Nevertheless, old-fashioned (ie, manual) monitoring systems are cumbersome, time-consuming, and never performed in real time. Therefore, a constant and computerized monitoring system is the only way to handle those metrics efficiently and in real time. In order to address these problems, a real-time freshwater shrimp farm monitoring system is presented in this article. This monitoring system integrates technologies such as microcontroller-based physical devices, internet of things, and web applications, that allow users to remotely monitor a shrimp farm, as well as to receive alerts when an out-of-range water parameter (ie, temperature, pH, dissolved oxygen, salinity level, and turbidity of water) is detected. The physical implementation of this system consists of a set of sensors that allow for collecting data about the water metrics of shrimp farm. This system was evaluated to test its effectiveness in terms of the size, weight, and the percentage of survival of the shrimp achieved when the shrimp culture pond is monitored by this system.

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“…More technical details on “SAM‐IoT" is available in Reference 11. Similar types of monitoring systems are found in References 12‐14.…”

Section: Background and Descriptionsupporting

confidence: 61%

Freshwater shrimp farm monitoring system for Bangladesh based on internet of things

Uddin

Istiaq

Rasadin

et al. 2020

Engineering Reports

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“…Thus, they cannot be monitored nor controlled via the Internet. Meanwhile, the two different culturing system"s parameters [14], [17] can be measured and monitored through the Internet but not corrected. An IoTbased Spirulina [18] and microalgae [19]- [20] culturing system where its parameters can be monitored and corrected through the Internet were developed but the growth of the algae was measured using turbidity value [18] and microalgae biomass [19]- [20], respectively.…”

Section: Challenges and Limitations Of Existing Approachesmentioning

confidence: 99%

IoT-based Closed Algal Cultivation System with Vision System for Cell Count through ImageJ via Raspberry Pi

Tolentino¹,

Belarmino²,

Chan³

et al. 2021

IJACSA

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Spirulina platensis and other microalgae are now being considered in the different fields of research. It is due to the former's endless potential let alone, its high protein content. That is why, a stable demand of microalga production is now necessary. To achieve a high-protein spirulina, its cultivation using closed algal cultivation requires monitoring and maintenance of the bio-environmental factors and parameters affecting its growth to provide a stable and efficient production of microalgae. Meanwhile, laboratories that culture spirulina determine its cell count through manually counting the cells under a microscopea tedious work. This establishes the need to construct a device that cultivates spirulina with maintaining and cell counting capabilities. Thus, the proponents developed a culturing device that has three main systems. The first system is tasked to maintain the bio-environmental parameters, such as the pH level, temperature, and light. The second system on the other hand speaks of the cell counting system through ImageJ's Image Processing. This system verifies the cell count and growth through counting the filaments of the spirulina. Lastly, a corresponding Android application, which was developed using Firebase and Android Studio, displays real-time values of the culture's parameter. Results show that the device was able to stabilize its parameters. Also, red LEDs exhibited 28.43% higher approximate cell count than red-blue LEDs. With this, the quality of the Spirulina that was produced throughout the study was improved. Lastly, the use of ImageJ's image processing feature showed no significant difference with manual counting. It also releases the results multiple times faster than the manual counting. Thus, being a better alternative to manual cell counting.

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“…are on the rise worldwide (Amaral, Vranic and Lal Das, 2020). These types of approaches have also been used to track biodiversity and wildlife conservation (Pettorelli et al, 2019;Wall et al, 2014), water systems and agriculture (Lafont et al, 2019), education (Williamson, 2016) and beyond. This has led to also the rise of predictive analytics and machine learning that utilise big data.…”

Section: Real-time Data Monitoring and Predictive Analyticsmentioning

confidence: 99%

Anticipatory innovation governance

Tõnurist¹,

Hanson²

2020

OECD Working Papers on Public Governance

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Back to the future: IoT to improve aquaculture : Real-time monitoring and algorithmic prediction of water parameters for aquaculture needs

Cited by 36 publications

References 2 publications

Freshwater shrimp farm monitoring system for Bangladesh based on internet of things

Freshwater shrimp farm monitoring system for Bangladesh based on internet of things

IoT-based Closed Algal Cultivation System with Vision System for Cell Count through ImageJ via Raspberry Pi

Anticipatory innovation governance

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