Fish feeding intensity quantification using machine vision and a lightweight 3D ResNet-GloRe network

2022/04/12 by Shuangxing Feng, Xinting Yang, Yang Liu +4 · 74 citations
Agricultural and Biological Sciences · Environmental Science · #Artificial intelligence #Artificial neural network #Computer science #FLOPS #Fish Ecology and Management Studies #Parallel computing #Pattern recognition (psychology) #Real-time computing #Residual neural network #Simulation #Sliding window protocol #Smart Agriculture and AI #Water Quality Monitoring Technologies #Window (computing)

paper · doi:10.1016/j.aquaeng.2022.102244

published in Aquacultural Engineering 98, 102244 (Elsevier BV)

openalex publication_date 2022/04/12 · crossref created 2022/04/12 · crossref issued 2022/08/01 · crossref published 2022/08/01 · crossref published-print 2022/08/01 · crossref deposited 2025/09/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29 · crossref indexed 2026/08/04

Citations

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Development of an adaptive neural-based fuzzy inference system for feeding decision-making assessment in silver perch (Bidyanus bidyanus) culture
• ConvNet Architecture Search for Spatiotemporal Feature Learning
• Deep learning
• Evaluation of fish feeding intensity in aquaculture using a convolutional neural network and machine vision
• Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories
• Using an EfficientNet-LSTM for the recognition of single Cow’s motion behaviours in a complicated environment

Cited by

• Feeding intensity assessment of aquaculture fish using Mel Spectrogram and deep learning algorithms
• STCA-MobileViTv3: A spatiotemporal collaborative attention network for fish feeding intensity recognition in underwater videos