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AI-Driven Approaches in Aquaculture Disease Management: Innovations, Challenges, and Future Directions
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Author(s): Naglaa F. Elbaz (Veterinary Medicine Directorate, Kafr Elsheikh, Egypt), Rana Ashraf (Damietta University, Egypt), Omnia Raied (Damietta University, Egypt)and Sohaila Elshamey (Damietta University, Egypt)
Copyright: 2025
Pages: 22
Source title:
The Role of Artificial Intelligence in Advancing Applied Life Sciences
Source Author(s)/Editor(s): Tamer Emara (Damietta University, Egypt), Esraa Hassan (Kafrelsheikh University, Egypt), Thanh Trinh (Phenikaa University, Vietnam), Genghui Li (Shenzhen University, China)and Abeer Saber (Damietta University, Egypt)
DOI: 10.4018/979-8-3693-9208-9.ch005
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Abstract
Aquaculture plays a vital role in global food security by providing a significant source of protein. However, disease outbreaks remain a persistent challenge, causing substantial economic losses. This chapter explores the transformative role of Artificial Intelligence (AI) in fish disease management, emphasizing machine learning and deep learning techniques such as Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs). By analyzing diverse data sources, including images, water quality parameters, and fish behavior, AI models offer early and accurate disease detection, enabling timely interventions. We highlight research findings from Kafr El-Sheikh, Egypt, that underscore the prevalence of bacterial diseases in freshwater aquaculture and the importance of AI-assisted diagnostic tools. Additionally, we examine case studies demonstrating the practical applications of AI in disease management, discuss current challenges, and identify future research directions to further enhance aquaculture sustainability and productivity.
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