Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Divergent filtration mechanisms of fibrous and non-fibrous microplastics in towing-net sampling toward a harmonized framework for abundance correction.

Environmental pollution (Barking, Essex : 1987)·2026
Same author

Revitalizing Fulleropyrrolidine via Nonionic Sidechain Engineering: An Ethanol-Processible Interlayer Enabling Efficient Organic Solar Cells.

Angewandte Chemie (International ed. in English)·2026
Same author

The Potential Role of Baseline FT3/FT4 Ratio as a Prognostic Biomarker for Patients With Ischemic Non-Obstructive Coronary Artery Disease.

Clinical cardiology·2026
Same author

A first quantitative assessment of seabed litter collection efficiency using bottom otter trawls and fishery depletion models.

Environmental pollution (Barking, Essex : 1987)·2026
Same author

Prediction of Neurological Functional Recovery after Carotid Endarterectomy Using Machine Learning and Carotid Computed Tomography Angiography Radiomics.

World neurosurgery·2026
Same author

Application of an IEW-CRITIC-CoCoSo method based on interval-valued T-spherical fuzzy for optimizing process parameters of 3D printed recycled polypropylene composites.

Scientific reports·2026
Same journal

Correction: Gernhardt et al. Ex Vivo Computed Tomographic Morphometry and Motion of the Native and Fractured Equine Accessory Carpal Bone. <i>Animals</i> 2026, <i>16</i>, 1132.

Animals : an open access journal from MDPI·2026
Same journal

Camera-Trap Assessment of Terrestrial Mammals and Ground-Dwelling Birds in the Zhangjiajie Chinese Giant Salamander National Nature Reserve, China.

Animals : an open access journal from MDPI·2026
Same journal

Beyond the Mission: Long-Term Endocrine Dynamics in Search and Rescue Dog-Handler Teams.

Animals : an open access journal from MDPI·2026
Same journal

Phenotypic Characterisation of the Abruzzo Donkey (<i>Equus asinus</i>), an Endangered Italian Genetic Resource: Body Measurements.

Animals : an open access journal from MDPI·2026
Same journal

Assessment of Maternal Genetic Diversity and Mitochondrial Population Structure of Endangered Indigenous Chicken Breeds in China.

Animals : an open access journal from MDPI·2026
Same journal

Effects of Expected Progeny Difference and Feeding Systems on Carcass Characteristics in Hanwoo Steers.

Animals : an open access journal from MDPI·2026
See all related articles

Related Experiment Video

Updated: Mar 15, 2026

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish
10:56

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Published on: March 6, 2014

13.1K

Edge-Deployable Fish Feeding-State Quantification and Recognition via Frame-Pair Motion Encoding and

Yuchen Xiao1, Weijia Ren1, Yining Wang1

  • 1College of Fisheries, Ocean University of China, Qingdao 266003, China.

Animals : an Open Access Journal From MDPI
|March 14, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient, edge-deployable framework for monitoring fish feeding states in aquaculture. The system uses optical flow and a lightweight network to accurately recognize feeding behavior, enabling data-driven decisions for improved farm management.

Keywords:
aquaculture monitoringfish feeding statefish welfareoptical flowreal-time recognition

More Related Videos

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus
05:57

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

Published on: April 8, 2019

7.3K
High-fat Feeding Paradigm for Larval Zebrafish: Feeding, Live Imaging, and Quantification of Food Intake
11:30

High-fat Feeding Paradigm for Larval Zebrafish: Feeding, Live Imaging, and Quantification of Food Intake

Published on: October 27, 2016

11.2K

Related Experiment Videos

Last Updated: Mar 15, 2026

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish
10:56

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Published on: March 6, 2014

13.1K
Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus
05:57

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

Published on: April 8, 2019

7.3K
High-fat Feeding Paradigm for Larval Zebrafish: Feeding, Live Imaging, and Quantification of Food Intake
11:30

High-fat Feeding Paradigm for Larval Zebrafish: Feeding, Live Imaging, and Quantification of Food Intake

Published on: October 27, 2016

11.2K

Area of Science:

  • Aquaculture technology
  • Computer vision
  • Animal behavior analysis

Background:

  • Accurate feeding-state monitoring is crucial for efficient aquaculture management, reducing waste, and ensuring fish welfare.
  • Current vision-based methods face limitations due to subjective labeling and high computational costs, hindering practical application.

Purpose of the Study:

  • To develop an objective, edge-deployable framework for real-time feeding-state quantification and recognition in aquaculture.
  • To enable timely, data-driven feeding decisions through automated motion analysis.

Main Methods:

  • Integration of frame-pair dense optical-flow encoding with a lightweight neural network (EfficientFeedingNet).
  • Utilizing an optical-flow-derived motion-intensity signal (V-Value) for automatic delineation of feeding intervals.
  • Construction of a perception-based dataset (Perceptual Dataset) with reproducible labels.

Main Results:

  • Models trained on the Perceptual Dataset achieved over 90% test accuracy, outperforming those trained on observer-labeled data.
  • The EfficientFeedingNet achieved 96.53% test accuracy and operated at 143.24 fps on edge hardware (Jetson Orin NX).
  • The framework demonstrated a practical basis for real-time, motion-driven feeding-state quantification.

Conclusions:

  • The proposed framework offers a practical solution for objective, real-time feeding-state monitoring in aquaculture.
  • EfficientFeedingNet's lightweight design facilitates edge deployment, supporting precision aquaculture practices.
  • This technology can significantly improve feeding management, reduce feed waste, and enhance fish welfare.