Big Data Analytics in Aquaculture Using Hive and Hadoop Platform

Rao, P. Venkateswara; Reddy, A. Ramamohan; Sucharita, V.

doi:10.4018/978-1-5225-2947-7.ch002

Cited by 3 publications

(1 citation statement)

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“…Various biosensors for fish health checks have been developed to increase the safety of cultured fish sold on the market. In general, physiological stress in fish results in two responses: first, a change in the concentration of certain hormones, such as cortisol and catecholamine, and second, a change in blood glucose levels brought on by the metabolic activation of the stress hormones (Rao et al, 2018). Cortisol has been extensively researched as a fish stress indicator (Mazeaud et al, 1977).…”

Section: Biosensor For Detection Of Fish Pathogensmentioning

confidence: 99%

Cutting‐edge technologies for detecting and controlling fish diseases: Current status, outlook, and challenges

Islam,

Ahammad,

Mohammed

2024

J World Aquaculture Soc

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Aquaculture is now the main source of seafood in human diets and is one of its fastest‐growing industries worldwide. However, the industry is facing several difficulties, including infectious diseases, the most significant limiting factor for aquaculture expansion. The impact of diseases on aquaculture growth, fecundity, mortality rates, and marketability is profound. Hence, the ability to predict disease outbreaks is crucial to overcoming these challenges. Various infectious agents such as bacteria, viruses, fungi, and parasites can cause significant losses of fish in intensive aquaculture practices. In an aquaculture environment, the high host density coupled with restricted water flow promotes pathogen spread. Early detection of disease is crucial for farmers as mortality rates can reach as high as 100% if left untreated. Therefore, new techniques and technical solutions for disease management in aquaculture are required. In this context, data analytics technologies, such as internet of things (IoT) sensors, artificial intelligence, and machine learning, allow farmers to proactively monitor their farms and detect potential disease outbreaks before they strike. Here, we highlighted the potential of machine learning algorithms in early pathogen detection and the possibilities of intelligent aquaculture in controlling disease outbreaks at the farm level. IoT is currently a popular study topic for smarter and sustainable aquaculture, as seen by the growing interest and broad overall assumptions. Therefore, this review aims to provide comprehensive information on the various aspects and challenges associated with modern technologies for controlling pathogenic microorganisms, as well as the potential benefits of using the IoT to improve fish health and welfare in aquaculture.

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Section: Biosensor For Detection Of Fish Pathogensmentioning

confidence: 99%