Construction and Application of Big Data Platform and Model for the Detection and Warning of Aquatic Diseases
A special issue of Fishes (ISSN 2410-3888). This special issue belongs to the section "Fish Pathology and Parasitology".
Deadline for manuscript submissions: closed (16 April 2024) | Viewed by 2584
Special Issue Editors
Interests: aquaculture; aquatic animal medicine; smart fisheries
Interests: biology of the aquatic diseases; water born zoonotic diseases; the development of novel treatment of diseases
Special Issue Information
Dear Colleagues,
In recent years, diseases have occurred in the cultivation of aquatic animals. For decades, scientific researchers have conducted exploration and experiments; however, in actual fish farming production, diseases remain a significant problem. Each method is established under limited conditions, but in actual application, they need to be adjusted in real time due to environmental and other conditions, which is a great challenge for farmers. Having a generalizable and easy-to-grasp intelligent decision-making platform is thus highly desirable.
In recent years, the development of computational and intelligent technology has made it easier for people to store and process the data obtained during aquaculture. The data originate from various sources, including historical disease records, image and video data recording host morphology and behavior, aquaculture environment data detected by water quality detection sensors, the number and type of pathogenic microorganisms obtained via disease detection methods, as well as physiological and biochemical data related to diseases. The great volume of data generated provides researchers with the capacity to achieve aquatic disease detection and warning based on big data. Via the integration of big data with artificial intelligence and machine learning, this will enable an auxiliary decision-making platform to be established, reducing costs and labor and mitigating the threat of aquaculture disease.
This Special Issue aims to provide insights into using big data platforms and models to achieve the detection and warning of aquatic disease, and will lay the foundation for the successful control of aquaculture diseases in future industrial aquaculture processes.
We welcome the submission of full research articles, short communications, or review articles providing directions for future research or suggestions regarding how to better manage populations in a changed, future climate.
Prof. Dr. Fei Yin
Dr. Azmi Al-Jubury
Dr. Xiao Xie
Guest Editors
Manuscript Submission Information
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Keywords
- aquatic diseases
- big data
- artificial intelligence
- machine learning
- auxiliary treatment decision-making tool
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