Automatic counting and positioning of slaughter pigs within the pen using a convolutional neural network and video images
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Automatic counting and positioning of slaughter pigs within the pen using a convolutional neural network and video images. / Jensen, Dan Børge; Pedersen, Lene Juul.
I: Computers and Electronics in Agriculture, Bind 188, 106296, 2021.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Automatic counting and positioning of slaughter pigs within the pen using a convolutional neural network and video images
AU - Jensen, Dan Børge
AU - Pedersen, Lene Juul
N1 - Publisher Copyright: © 2021 The Author(s)
PY - 2021
Y1 - 2021
N2 - Pen fouling is an undesired behaviour of slaughter pigs, which increases labour costs for the farmer, worsens the hygiene and welfare of the pigs, and has negative environmental consequences. Previous research suggests that monitoring the positioning behaviour of grower/finisher pigs within their pen has the potential to be used in early warning systems that can alert the farmer to an impending pen fouling event 1–3 days in advance. For such a warning system to be feasible, monitoring of the pigs’ positioning behaviour must be automated. To this end, we present a novel yet relatively simple method, namely a convolutional neural network (CNN) with a single linear regression output node. The proposed CNN takes partial images of a pen, corresponding to the different areas of the pen, and outputs an estimated count of the number of pigs in the partial image. By inputting three partial images corresponding to the three areas of the pen, the model can estimate the number of pigs in each area. The trained CNN generally performs well when applied to data from unseen test pens, with mean absolute errors of less than 1 pig and coefficients of determination between observed and estimated counts above 0.9. In cases where the trained model underperforms on the test pens, fine-tuning by transfer learning can be applied; we show that an initially underperforming model can be fine-tuned on one day's worth of test set data (26 labelled images), after which it will produce near-perfect estimates on all subsequent days in the same test set.
AB - Pen fouling is an undesired behaviour of slaughter pigs, which increases labour costs for the farmer, worsens the hygiene and welfare of the pigs, and has negative environmental consequences. Previous research suggests that monitoring the positioning behaviour of grower/finisher pigs within their pen has the potential to be used in early warning systems that can alert the farmer to an impending pen fouling event 1–3 days in advance. For such a warning system to be feasible, monitoring of the pigs’ positioning behaviour must be automated. To this end, we present a novel yet relatively simple method, namely a convolutional neural network (CNN) with a single linear regression output node. The proposed CNN takes partial images of a pen, corresponding to the different areas of the pen, and outputs an estimated count of the number of pigs in the partial image. By inputting three partial images corresponding to the three areas of the pen, the model can estimate the number of pigs in each area. The trained CNN generally performs well when applied to data from unseen test pens, with mean absolute errors of less than 1 pig and coefficients of determination between observed and estimated counts above 0.9. In cases where the trained model underperforms on the test pens, fine-tuning by transfer learning can be applied; we show that an initially underperforming model can be fine-tuned on one day's worth of test set data (26 labelled images), after which it will produce near-perfect estimates on all subsequent days in the same test set.
KW - Convolutional neural network
KW - Growing pigs
KW - Machine vision
KW - Pen fouling
KW - Positioning behaviour
U2 - 10.1016/j.compag.2021.106296
DO - 10.1016/j.compag.2021.106296
M3 - Journal article
AN - SCOPUS:85110414193
VL - 188
JO - Computers and Electronics in Agriculture
JF - Computers and Electronics in Agriculture
SN - 0168-1699
M1 - 106296
ER -
ID: 275826777