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Applied Sciences
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Sustainable Aquaculture: Scientific Advances and Applications, 2nd Edition
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Sustainable Aquaculture: Scientific Advances and Applications, 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental Sciences".

Deadline for manuscript submissions: closed (20 June 2024) | Viewed by 743

Special Issue Editors

Dr. Tiago Verdelhos

E-Mail Website
Guest Editor
Department of Life Sciences, Marine and Environmental Sciences Center, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
Interests: marine, estuarine, and coastal ecosystems; biodiversity and ecosystem functioning; environmental risk; aquaculture and fisheries; ecological modeling; ecotoxicology
Special Issues, Collections and Topics in MDPI journals
Dr. Ana Cristina Rocha

E-Mail Website
Guest Editor
Department of Life Sciences, Marine and Environmental Sciences Center, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
Interests: marine, estuarine, and coastal ecosystems; ecotoxicology; environmental risk; water quality; remediation technologies; sustainable aquaculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce the second edition of this topic. Aquaculture is a fast-blooming food-production industry that recently overtook fisheries as the main source of fish for human consumption, representing more than 50% global fish production (82 million tons in 2018). Considering the nutritional needs of the growing human population, aquaculture production is expected to increase to 109 million tons by 2030. As such, this sector has great potential to expand and stimulate the economy and create jobs, promoting blue growth worldwide.

However, aquaculture is often associated with decreased environmental quality and has severe impacts on natural ecosystems, namely as a source of pollution (e.g., nutrient enrichment and chemical contamination) and as a vector that promotes ecological interactions with wild species (e.g., invasive species and diseases). This is a priority issue and, although great progress has been made in recent years, it is essential to continually improve environmental sustainability, which is vital to the long-term economic sustainability of aquaculture.

The scientific community plays a central role in sustainable aquaculture development through research and communication. On the one hand, fundamental and applied research is needed to increase knowledge and develop better practical solutions to environmental problems. On the other hand, R&D knowledge transfer is essential for the effective dissemination of results and best practices to the aquaculture sector. In this Special Issue, we invite the submission of contributions addressing the most recent advances and applications in sustainable aquaculture, encompassing research into the very “basics” of marine/aquatic ecology and processes, ongoing applied research, and case studies.

Dr. Tiago Verdelhos
Dr. Ana Cristina Rocha
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • aquaculture
  • sustainability
  • environment
  • blue growth
  • marine/aquatic ecology

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Published Papers (1 paper)

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Research

18 pages, 1937 KiB  
Article
Advancing Crayfish Disease Detection: A Comparative Study of Deep Learning and Canonical Machine Learning Techniques
by Yasin Atilkan, Berk Kirik, Koray Acici, Recep Benzer, Fatih Ekinci, Mehmet Serdar Guzel, Semra Benzer and Tunc Asuroglu
Appl. Sci. 2024, 14(14), 6211; https://doi.org/10.3390/app14146211 - 17 Jul 2024
Viewed by 353
Abstract
This study evaluates the effectiveness of deep learning and canonical machine learning models for detecting diseases in crayfish from an imbalanced dataset. In this study, measurements such as weight, size, and gender of healthy and diseased crayfish individuals were taken, and at least [...] Read more.
This study evaluates the effectiveness of deep learning and canonical machine learning models for detecting diseases in crayfish from an imbalanced dataset. In this study, measurements such as weight, size, and gender of healthy and diseased crayfish individuals were taken, and at least five photographs of each individual were used. Deep learning models outperformed canonical models, but combining both approaches proved the most effective. Utilizing the ResNet50 model for automatic feature extraction and subsequent training of the RF algorithm with these extracted features led to a hybrid model, RF-ResNet50, which achieved the highest performance in diseased sample detection. This result underscores the value of integrating canonical machine learning algorithms with deep learning models. Additionally, the ConvNeXt-T model, optimized with AdamW, performed better than those using SGD, although its disease detection sensitivity was 1.3% lower than the hybrid model. McNemar’s test confirmed the statistical significance of the performance differences between the hybrid and the ConvNeXt-T model with AdamW. The ResNet50 model’s performance was improved by 3.2% when combined with the RF algorithm, demonstrating the potential of hybrid approaches in enhancing disease detection accuracy. Overall, this study highlights the advantages of leveraging both deep learning and canonical machine learning techniques for early and accurate detection of diseases in crayfish populations, which is crucial for maintaining ecosystem balance and preventing population declines. Full article
(This article belongs to the Special Issue Sustainable Aquaculture: Scientific Advances and Applications, 2nd Edition)
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