Intelligent feeding control methods in aquaculture with an emphasis on fish: a review

By C. Zhou, Daming Xu, Lin Kai, Chuanheng Sun, Xinting Yang. 2018

187 citations recorded by the source corpus.

Subfields: Nature and Landscape Conservation, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/471f5f2e520c82610dd2429d692ecf3854bb59b1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Nature and Landscape Conservation

1. Katherine A. Sloman

Citations in the corpus

• Emerging indicators of fish welfare in aquaculture (2021, methodology): “It is also practical for use in high density rearing tanks, which are typical in aquaculture 101 , and only requires a minimal amount of technical equipment.”
• Deep learning for smart fish farming: applications, opportunities and challenges (2020, methodology): “The model evaluation index of this CNN model also improved compared with those of traditional manual feature extraction methods, such as FIFFB and SIFFB (Zhou et al., 2017b ; Zhou et al., 2019).”
• The importance of behaviour in improving the production of shrimp in aquaculture (2018, background): “However, for this to be a truly 178 effective solution there needs to be high feeding efficiency of individuals, which can be 179 confirmed by the collection of behavioural data (Zhou et al. 2017).”