A Review on Underwater Image Enhancement

Ashrafi, M.; Verma, Apurv; Badholia, Abhishek

doi:10.32628/ijsrset207313

Cited by 2 publications

(3 citation statements)

References 12 publications

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“…Researchers believe convolutional neural networks are the most cutting-edge method for achieving significant results in this area (Natarajan, 2017). More than 99% accuracy was achieved, which sets a high bar for future studies (Natarajan, 2017).…”

Section: Opportunities and Challengesmentioning

confidence: 99%

“…Studies in image processing have shown that deep learning algorithms outperform conventional approaches because of their speed, high automation characteristics, and overall performance. Researchers believe convolutional neural networks are the most cutting‐edge method for achieving significant results in this area (Natarajan, 2017). More than 99% accuracy was achieved, which sets a high bar for future studies (Natarajan, 2017).…”

Section: Image‐based Machine Learning Technique For Fish Disease Dete...mentioning

confidence: 99%

“…Researchers believe convolutional neural networks are the most cutting‐edge method for achieving significant results in this area (Natarajan, 2017). More than 99% accuracy was achieved, which sets a high bar for future studies (Natarajan, 2017). Similarly, the degree to which automation may be achieved within a specific time frame depends not just on state‐of‐the‐art technological advancement but also on the specifics of the disease itself.…”

Section: Image‐based Machine Learning Technique For Fish Disease Dete...mentioning

confidence: 99%

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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: Opportunities and Challengesmentioning

confidence: 99%

Section: Image‐based Machine Learning Technique For Fish Disease Dete...mentioning

confidence: 99%

Section: Image‐based Machine Learning Technique For Fish Disease Dete...mentioning

confidence: 99%

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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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A review on image processing for fish disease detection

Pauzi

Hassan

Yusoff

et al. 2021

J. Phys.: Conf. Ser.

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Fish disease is considered the main cause for production and economic losses by fish farmers. Fish disease detection and health monitoring is a demanding task by manual method of human visualization. Therefore, any potential approach that is fast, reliable and possesses high automation supports an interest in this issue. Nowadays, with the current emergence in the technology revolution, image processing has been extensively used in disease detection field, especially in human and plant, aiding the human experts in providing the right treatment. Image processing technique offers opportunities to improve the traditional approach in achieving accurate results. Besides, several steps in image processing are adopted including image acquisition, image pre-processing, image segmentation, object detection, feature extraction and classification. The objective of this paper is to briefly review the work established in the fish disease detection field with the use of numerous classification techniques of image processing, including rule-based expert system, machine learning, deep learning, statistical method and hybrid method. The present review recognizes the need for improvement in these image processing approaches that would be valuable for further advancement in terms of performance.

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A Review on Underwater Image Enhancement

Cited by 2 publications

References 12 publications

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

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

A review on image processing for fish disease detection

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