Session 59. CYBELE project on high performance computing for precision livestock farming
<< Back to the Session index of Davos 2021
Theatre Session
CYBELE: high performance computing for precision livestock farming
S. Davy
A two-step deep learning model for pen-level estimation of slaughter pig live weight distribution
D.B. Jensen and F. Hakansson
Video-based classification of agonistic behaviour in pigs using a combination of CNN and RNN
F. Hakansson and D. Jensen
Individual pig health monitoring using multivariate analysis on feeding, drinking and weight data
C. Vandenbussche, F. Castaldi, B. Sonck, J. Vangeyte and J. Maselyne
Evaluating machine learning techniques applied to the problem of boar taint
G. Makridis, E. Heyrman, L. Vanhaecke, F. Tuyttens, S. Janssens, N. Buys, M. Aluwé and F. Mavrepis
Enhancing in-line meat processing traceability using deep learning
R. Van De Vijver, F. Castaldi, B. Callens, M. Aluwé, R. Klont, J. Vangeyte and J. Maselyne
Prediction of pork meat quality parameters with existing hyperspectral devices
B.H.R. Callens, S. De Smet, S.R. Cool, F. Castaldi, R. Van De Vijver, E. Kowalski and M. Aluwé
Towards in-line fish species classification in beam-trawl fisheries for real time stock assessment
R. Van De Vijver, F. Castaldi, K. Sys, H. Polet, H. Lenoir, J. Vangeyte and J. Maselyne
Applying machine learning to production data from aquaculture to predict fish growth
S. Davy and K. Matuleviciute