Automatic Recognition of Fish Diseases in Fish Farms

Ahmed, Waqas; Medhat, Hadeer; Esmail, Mariam; Osama, Kareem; Samy, Radwa; Ghanim, Taraggy M.

doi:10.1109/icces48960.2019.9068141

Cited by 25 publications

(6 citation statements)

References 16 publications

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“…Sucipto et al compared the classification algorithms with the disease data of catfish and carp [ 94 ], and the results showed that the C4.5 algorithm could be used to evaluate fish disease performance. Waleed et al proposed automatically identifying three different types of fish diseases [ 95 ], and used different CNN architectures according to the different color spaces on the dataset images. The AlexNet architecture achieved superior results in the XYZ color space.…”

Section: Intelligent Diagnosis Methods Of Fish Diseases Based On Imagesmentioning

confidence: 99%

Advanced Techniques for the Intelligent Diagnosis of Fish Diseases: A Review

Wang

et al. 2022

Animals

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Aquatic products, as essential sources of protein, have attracted considerable concern by producers and consumers. Precise fish disease prevention and treatment may provide not only healthy fish protein but also ecological and economic benefits. However, unlike intelligent two-dimensional diagnoses of plants and crops, one of the most serious challenges confronted in intelligent aquaculture diagnosis is its three-dimensional space. Expert systems have been applied to diagnose fish diseases in recent decades, allowing for restricted diagnosis of certain aquaculture. However, this method needs aquaculture professionals and specialists. In addition, diagnosis speed and efficiency are limited. Therefore, developing a new quick, automatic, and real-time diagnosis approach is very critical. The integration of image-processing and computer vision technology intelligently allows the diagnosis of fish diseases. This study comprehensively reviews image-processing technology and image-based fish disease detection methods, and analyzes the benefits and drawbacks of each diagnostic approach in different environments. Although it is widely acknowledged that there are many approaches for disease diagnosis and pathogen identification, some improvements in detection accuracy and speed are still needed. Constructing AR 3D images of fish diseases, standard and shared datasets, deep learning, and data fusion techniques will be helpful in improving the accuracy and speed of fish disease diagnosis.

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Section: Intelligent Diagnosis Methods Of Fish Diseases Based On Imagesmentioning

confidence: 99%

Advanced Techniques for the Intelligent Diagnosis of Fish Diseases: A Review

Wang

et al. 2022

Animals

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“…Recently, researchers proposed a mobile AR application with a combination of image processing technique models taking one EUS diseased fish. This model successfully detects diseased fish at ground levels (Waleed et al, 2019). This fish 3D model of various sizes is used in Puntius chola , AR applications for visually comparing and diagnosing diseased fish on the go.…”

Section: Application Of Armentioning

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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“…The diagnosis accuracy of PCA-FAST-NN is 86%. Some approaches used the Raspberry Pi kit to control the camera to obtain the video data of the fish in the culture pond (Waleed et al 2019). The image preprocessing stage built a data set of three color spaces (RGB, Ycbcr, XYZ).…”

Section: Diagnosis Of Fish Diseasesmentioning

confidence: 99%

Intelligent fish farm—the future of aquaculture

Wang

et al. 2021

Aquacult Int

112

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With the continuous expansion of aquaculture scale and density, contemporary aquaculture methods have been forced to overproduce resulting in the accelerated imbalance rate of water environment, the frequent occurrence of fish diseases, and the decline of aquatic product quality. Moreover, due to the fact that the average age profile of agricultural workers in many parts of the world are on the higher side, fishery production will face the dilemma of shortage of labor, and aquaculture methods are in urgent need of change. Modern information technology has gradually penetrated into various fields of agriculture, and the concept of intelligent fish farm has also begun to take shape. The intelligent fish farm tries to deal with the precise work of increasing oxygen, optimizing feeding, reducing disease incidences, and accurately harvesting through the idea of “replacing human with machine,” so as to liberate the manpower completely and realize the green and sustainable aquaculture. This paper reviews the application of fishery intelligent equipment, IoT, edge computing, 5G, and artificial intelligence algorithms in modern aquaculture, and analyzes the existing problems and future development prospects. Meanwhile, based on different business requirements, the design frameworks for key functional modules in the construction of intelligent fish farm are proposed.

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Automatic Recognition of Fish Diseases in Fish Farms

Cited by 25 publications

References 16 publications

Advanced Techniques for the Intelligent Diagnosis of Fish Diseases: A Review

Advanced Techniques for the Intelligent Diagnosis of Fish Diseases: A Review

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

Intelligent fish farm—the future of aquaculture

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