Dr Alzayat Saleh ~ Postdoctoral Research Fellow, Deep Learning for Agriculture

Alzayat Saleh is a Postdoctoral Research Fellow, Deep Learning for Agriculture at James Cook University and a passionate researcher in deep learning for aquaculture and marine sciences. He has published 15 articles on deep learning in prestigious journals and conferences, with over 200 citations. Alzayat's research interests include developing novel deep learning methods for underwater fish detection, segmentation, tracking, and landmark detection. He has also received several awards and scholarships for his research excellence. Alzayat enjoys collaborating with other researchers and practitioners in the field of deep learning and aquaculture.

Interests

Research

• Deep Learning for Agriculture

Research Disciplines

• Machine learning (4611)
• Computer vision and multimedia computation (4603)
• Fisheries sciences (3005)

Socio-Economic Objectives

• Fisheries - aquaculture (1002)
• Clinical health (2001)

These are the most recent publications associated with this author. To see a detailed profile of all publications stored at JCU, visit ResearchOnline@JCU. Hover over Altmetrics badges to see social impact.

Journal Articles

• Saleh A, Hasan M, Raadsma H, Khatkar M, Jerry D and Rahimi Azghadi M (in press) Prawn Morphometrics and Weight Estimation from Images using Deep Learning for Landmark Localization. Aquacultural Engineering,
• Saleh A, Sheaves M, Jerry D and Rahimi Azghadi M (2024) Applications of deep learning in fish habitat monitoring: A tutorial and survey. Expert Systems with Applications, 238.
• Saleh A, Jones D, Jerry D and Azghadi M (2023) MFLD-net: a lightweight deep learning network for fish morphometry using landmark detection. Aquatic Ecology, 57. pp. 913-931
• Saleh A, Sheaves M and Rahimi Azghadi M (2022) Computer vision and deep learning for fish classification in underwater habitats: A survey. Fish and Fisheries, 23 (4). pp. 977-999
• Laradji I, Saleh A, Rodriguez P, Nowrouzezahrai D, Rahimi Azghadi M and Vazquez D (2021) Weakly supervised underwater fish segmentation using affinity LCFCN. Scientific Reports, 11. p. 17379
• Saleh A, Laradji I, Lammie C, Vazquez D, Flavell C and Rahimi Azghadi M (2021) A deep learning localization method for measuring abdominal muscle dimensions in ultrasound images. IEEE Journal of Biomedical and Health Informatics, 25. pp. 3865-3873
• Saleh A, Laradji I, Konovalov D, Bradley M, Vazquez D and Sheaves M (2020) A realistic fish-habitat dataset to evaluate algorithms for underwater visual analysis. Scientific Reports, 10.
• Konovalov D, Saleh A, Domingos J, White R and Jerry D (2018) Estimating mass of harvested Asian seabass Lates calcarifer from images. World Journal of Engineering and Technology, 6 (3). pp. 15-23

Conference Papers

• Konovalov D, Saleh A, Efremova D, Domingos J and Jerry D (2019) Automatic weight estimation of harvested fish from images. Proceedings of the International Conference on Digital Image Computing. In: DICTA 2019: International Conference on Digital Image Computing: Techniques and Applications, 2-4 December 2019, Perth, WA, Australia
• Konovalov D, Saleh A, Bradley M, Sankupellay M, Marini S and Sheaves M (2019) Underwater fish detection with weak multi-domain supervision. Proceedings of the International Joint Conference on Neural Networks. In: 2019 IJCNN: International Joint Conference on Neural Networks, 14-19 July 2019, Budapest, Hungary

These are the most recent metadata records associated with this researcher. To see a detailed description of all dataset records, visit Research Data Australia.

• Saleh, A. (2020) DeepFish: A realistic fish-habitat dataset to evaluate algorithms for underwater visual analysis. James Cook University

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