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Exploring opportunities of Artificial Intelligence in aquaculture to meet increasing food demand.
Ashraf Rather, Mohd; Ahmad, Ishtiyaq; Shah, Azra; Ahmad Hajam, Younis; Amin, Adnan; Khursheed, Saba; Ahmad, Irfan; Rasool, Showkat.
Afiliação
  • Ashraf Rather M; Division of Fish Genetics and Biotechnology, Faculty of Fisheries Ganderbal, Sher-e- Kashmir University of Agricultural Science and Technology, Kashmir 190006, India.
  • Ahmad I; Division of Fish Genetics and Biotechnology, Faculty of Fisheries Ganderbal, Sher-e- Kashmir University of Agricultural Science and Technology, Kashmir 190006, India.
  • Shah A; Division of Fish Genetics and Biotechnology, Faculty of Fisheries Ganderbal, Sher-e- Kashmir University of Agricultural Science and Technology, Kashmir 190006, India.
  • Ahmad Hajam Y; Department of Life Sciences and Allied Health Sciences, Sant Baba Bhag Singh University, Jalandhar, Punjab, India.
  • Amin A; Division of Aquatic Environmental Management, Faculty of Fisheries, Rangil, Ganderbal, SKUAST-Kashmir, 190006, India.
  • Khursheed S; Division of Fish Genetics and Biotechnology, Faculty of Fisheries Ganderbal, Sher-e- Kashmir University of Agricultural Science and Technology, Kashmir 190006, India.
  • Ahmad I; Department of Zoology, School of Bioengineering & Biosciences, Lovely Professional University, Phagwara, Punjab 144411, India.
  • Rasool S; Division of Fish Genetics and Biotechnology, Faculty of Fisheries Ganderbal, Sher-e- Kashmir University of Agricultural Science and Technology, Kashmir 190006, India.
Food Chem X ; 22: 101309, 2024 Jun 30.
Article em En | MEDLINE | ID: mdl-38550881
ABSTRACT
The increasing global population drives a rising demand for food, particularly fish as a preferred protein source, straining capture fisheries. Overfishing has depleted wild stocks, emphasizing the need for advanced aquaculture technologies. Unlike agriculture, aquaculture has not seen substantial technological advancements. Artificial Intelligence (AI) tools like Internet of Things (IoT), machine learning, cameras, and algorithms offer solutions to reduce human intervention, enhance productivity, and monitor fish health, feed optimization, and water resource management. However, challenges such as data collection, standardization, model accuracy, interpretability, and integration with existing aquaculture systems persist. This review explores the adoption of AI techniques and tools to advance the aquaculture industry and bridge the gap between food supply and demand.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Food Chem X Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Food Chem X Ano de publicação: 2024 Tipo de documento: Article