Recent advances of deep learning algorithms for aquacultural machine vision systems with emphasis on fish-Reference-Cited by-同舟云学术

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Recent advances of deep learning algorithms for aquacultural machine vision systems with emphasis on fish

Published:2021-11-23 Issue: Volume: Page:
ISSN:0269-2821
Container-title:Artificial Intelligence Review
language:en
Short-container-title:Artif Intell Rev

Author:

Li Daoliang^ORCID,Du Ling

Funder

Next generation precision aquaculture: R&D on intelligent measurement, control and equipment technologies

Research on the rapid detection mechanism and method of trace-level toxic nitrogen in aquaculture water based on SERS optopole

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Linguistics and Language,Language and Linguistics

Link

https://link.springer.com/content/pdf/10.1007/s10462-021-10102-3.pdf

Reference137 articles.

1. Abayomi‐Alli OO, Damaševičius R, Misra S, Maskeliūnas R (2021) Cassava disease recognition from low‐quality images using enhanced data augmentation model and deep learning. Expert Syst 38(7):1–21

2. Al-Ghaib H, Adhami R (2014) On the digital image additive white Gaussian noise estimation. In: 2014 international conference on industrial automation, information and communications technology, pp 90–96

3. Ali-Gombe A, Elyan E, Jayne C (2017) Fish classification in context of noisy images. In: International conference on engineering applications of neural networks, pp 216–226

4. Álvarez-Ellacuría A, Palmer M, Catalán IA, Lisani J-L (2019) Image-based, unsupervised estimation of fish size from commercial landings using deep learning. ICES J Mar Sci 77(4):1330–1339

5. Ashley PJ (2007) Fish welfare: current issues in aquaculture. Appl Anim Behav Sci 104(3–4):199–235

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