TY - GEN
T1 - Calving prediction from video
T2 - 9th European Conference on Precision Livestock Farming, ECPLF 2019
AU - Sugawara, K.
AU - Saito, S.
AU - Nakano, T.
AU - Akabane, M.
AU - Kobayashi, T.
AU - Ogawa, T.
N1 - Publisher Copyright:
© Precision Livestock Farming 2019 - Papers Presented at the 9th European Conference on Precision Livestock Farming, ECPLF 2019. All rights reserved.
PY - 2019
Y1 - 2019
N2 - Behavioural information relevant to calving is extracted and exploited successfully for automatic calving prediction from videos. Calving prediction is key for preventing fatal accidents such as stillbirth and dystocia. Such a prediction has been performed using contact sensors to capture a cow's typical movements before calving. However, directly attaching sensors to a cow's body is not desirable from the viewpoint of animal welfare, economic load, and safety for livestock farmers. This paper presents a camera-based, noncontact calving prediction system that captures typical precalving movements such as rotations, turns and step-backs. The information on the frequency of such behaviours and that on the frequency of changes in the behaviours are extracted every few minutes using deep neural networks and used as inputs to a calving predictor based on support vector machines. Experimental comparisons conducted using the videos of four Japanese black beef cows of normal and precalving statuses demonstrated that the system developed with five cows' videos achieved a precision rate of 97% and a recall rate of 82% for a cow that was active before calving.
AB - Behavioural information relevant to calving is extracted and exploited successfully for automatic calving prediction from videos. Calving prediction is key for preventing fatal accidents such as stillbirth and dystocia. Such a prediction has been performed using contact sensors to capture a cow's typical movements before calving. However, directly attaching sensors to a cow's body is not desirable from the viewpoint of animal welfare, economic load, and safety for livestock farmers. This paper presents a camera-based, noncontact calving prediction system that captures typical precalving movements such as rotations, turns and step-backs. The information on the frequency of such behaviours and that on the frequency of changes in the behaviours are extracted every few minutes using deep neural networks and used as inputs to a calving predictor based on support vector machines. Experimental comparisons conducted using the videos of four Japanese black beef cows of normal and precalving statuses demonstrated that the system developed with five cows' videos achieved a precision rate of 97% and a recall rate of 82% for a cow that was active before calving.
KW - Behavioural features
KW - Calving prediction
KW - Deep neural network
KW - Precision livestock farming
UR - http://www.scopus.com/inward/record.url?scp=85073752889&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85073752889&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85073752889
T3 - Precision Livestock Farming 2019 - Papers Presented at the 9th European Conference on Precision Livestock Farming, ECPLF 2019
SP - 663
EP - 669
BT - Precision Livestock Farming 2019 - Papers Presented at the 9th European Conference on Precision Livestock Farming, ECPLF 2019
A2 - O'Brien, Bernadette
A2 - Hennessy, Deirdre
A2 - Shalloo, Laurence
PB - Organising Committee of the 9th European Conference on Precision Livestock Farming (ECPLF), Teagasc, Animal and Grassland Research and Innovation Centre
Y2 - 26 August 2019 through 29 August 2019
ER -