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https://doi.org/10.2139/ssrn.4524867

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inferredcites →https://doi.org/10.1007/s12562-009-0162-5openalex-extracted
Automated acoustic method for counting and sizing farmed fish during transfer using DIDSON
inferredcites →https://doi.org/10.1016/0144-8609(91)90024-eopenalex-extracted
Hydroacoustic detection of food waste — A method to estimate maximum food intake of fish populations in sea cages
inferredcites →https://doi.org/10.1016/0144-8609(94)00006-mopenalex-extracted
Detection and counting of uneaten food pellets in a sea cage using image analysis
inferredcites →https://doi.org/10.1016/j.aquaculture.2008.04.033openalex-extracted
Effects of fish bioturbation on the vertical distribution of water temperature and dissolved oxygen in a fish culture-integrated waste stabilization pond system in Vietnam
inferredcites →https://doi.org/10.1016/j.aquaculture.2016.07.037openalex-extracted
The oxygen threshold for maximal feed intake of Atlantic salmon post-smolts is highly temperature-dependent
inferredcites →https://doi.org/10.1016/j.aquaculture.2018.04.064openalex-extracted
Modified motion influence map and recurrent neural network-based monitoring of the local unusual behaviors for fish school in intensive aquaculture
inferredcites →https://doi.org/10.1016/j.aquaculture.2018.07.068openalex-extracted
Adaptive neural fuzzy inference system for feeding decision-making of grass carp (Ctenopharyngodon idellus) in outdoor intensive culturing ponds
inferredcites →https://doi.org/10.1016/j.aquaculture.2019.04.056openalex-extracted
Evaluation of fish feeding intensity in aquaculture using a convolutional neural network and machine vision
inferredcites →https://doi.org/10.1016/j.aquaculture.2020.734970openalex-extracted
Feed requirement determination of grass carp (Ctenopharyngodon idella) using a hybrid method of bioenergetics factorial model and fuzzy logic control technology under outdoor pond culturing systems
inferredcites →https://doi.org/10.1016/j.aquaculture.2020.735508openalex-extracted
Automatic recognition methods of fish feeding behavior in aquaculture: A review
inferredcites →https://doi.org/10.1016/j.aquaculture.2021.737215openalex-extracted
Behavioral spatial-temporal characteristics-based appetite assessment for fish school in recirculating aquaculture systems
inferredcites →https://doi.org/10.1016/j.aquaeng.2014.03.005openalex-extracted
Measuring feeding activity of fish in RAS using computer vision
inferredcites →https://doi.org/10.1016/j.aquaeng.2015.02.001openalex-extracted
Development of an adaptive neural-based fuzzy inference system for feeding decision-making assessment in silver perch (Bidyanus bidyanus) culture
inferredcites →https://doi.org/10.1016/j.aquaeng.2017.07.008openalex-extracted
Assessing appetite of the swimming fish based on spontaneous collective behaviors in a recirculating aquaculture system
inferredcites →https://doi.org/10.1016/j.aquaeng.2019.102000openalex-extracted
Integration of sonar and optical camera images using deep neural network for fish monitoring
inferredcites →https://doi.org/10.1016/j.aquaeng.2021.102178openalex-extracted
Evaluating fish feeding intensity in aquaculture with convolutional neural networks
inferredcites →https://doi.org/10.1016/j.aquaeng.2022.102244openalex-extracted
Fish feeding intensity quantification using machine vision and a lightweight 3D ResNet-GloRe network
inferredcites →https://doi.org/10.1016/j.aquaeng.2023.102338openalex-extracted
MSIF-MobileNetV3: An improved MobileNetV3 based on multi-scale information fusion for fish feeding behavior analysis
inferredcites →https://doi.org/10.1016/j.compag.2017.02.013openalex-extracted
Near-infrared imaging to quantify the feeding behavior of fish in aquaculture
inferredcites →https://doi.org/10.1016/j.compag.2018.02.006openalex-extracted
Near infrared computer vision and neuro-fuzzy model-based feeding decision system for fish in aquaculture
inferredcites →https://doi.org/10.1016/j.compag.2021.106135openalex-extracted
Real-time detection of uneaten feed pellets in underwater images for aquaculture using an improved YOLO-V4 network
inferredcites →https://doi.org/10.1016/j.compag.2021.106316openalex-extracted
A dual attention network based on efficientNet-B2 for short-term fish school feeding behavior analysis in aquaculture
inferredcites →https://doi.org/10.1016/j.fishres.2006.07.005openalex-extracted
Temperature affects activity and feeding motivation in Pacific halibut: Implications for bait-dependent fishing
inferredcites →https://doi.org/10.1016/j.patcog.2022.109132openalex-extracted
Data augmentation for univariate time series forecasting with neural networks
inferredcites →https://doi.org/10.1016/j.scitotenv.2012.07.073openalex-extracted
Monitoring the effects of floods on submerged macrophytes in a large river
inferredcites →https://doi.org/10.1109/cvpr.2007.382977openalex-extracted
A Lagrangian Particle Dynamics Approach for Crowd Flow Segmentation and Stability Analysis
inferredcites →https://doi.org/10.1109/icinfa.2011.5949043openalex-extracted
Abnormal crowd behavior detection based on the energy model
inferredcites →https://doi.org/10.1109/jsen.2022.3151777openalex-extracted
A Computer Vision-Based Intelligent Fish Feeding System Using Deep Learning Techniques for Aquaculture
inferredcites →https://doi.org/10.1109/tcsvt.2015.2395752openalex-extracted
Motion Influence Map for Unusual Human Activity Detection and Localization in Crowded Scenes
inferredcites →https://doi.org/10.1111/raq.12218openalex-extracted
Intelligent feeding control methods in aquaculture with an emphasis on fish: a review
inferredcites →https://doi.org/10.1186/s40537-019-0197-0openalex-extracted
A survey on Image Data Augmentation for Deep Learning
inferredcites →https://doi.org/10.4028/www.scientific.net/amm.303-306.1200openalex-extracted
Research of Intelligent Control System for Fish-Farming
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