DigiPig: First Developments of an Automated Monitoring System for Body, Head and Tail Detection in Intensive Pig Farming

Marko Ocepek, Anja Žnidar, Miha Lavrič, Dejan Škorjanc and Inger Lise Andersen
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Marko Ocepek: Department of Animal and Aquacultural Sciences, Faculty of Biosciences, Norwegian University of Life Sciences, 1432 Ås, Norway
Anja Žnidar: Department of Animal and Aquacultural Sciences, Faculty of Biosciences, Norwegian University of Life Sciences, 1432 Ås, Norway
Miha Lavrič: Research Group Ambient Intelligence, Saxion University of Applied Sciences, P.O. Box 70.000, 7500 KB Enschede, The Netherlands
Dejan Škorjanc: Faculty of Agriculture and Life Sciences, University of Maribor, Pivola 10, 2311 Hoce, Slovenia
Inger Lise Andersen: Department of Animal and Aquacultural Sciences, Faculty of Biosciences, Norwegian University of Life Sciences, 1432 Ås, Norway

Agriculture, 2021, vol. 12, issue 1, 1-12

Abstract: The goal of this study was to develop an automated monitoring system for the detection of pigs’ bodies, heads and tails. The aim in the first part of the study was to recognize individual pigs (in lying and standing positions) in groups and their body parts (head/ears, and tail) by using machine learning algorithms (feature pyramid network). In the second part of the study, the goal was to improve the detection of tail posture (tail straight and curled) during activity (standing/moving around) by the use of neural network analysis (YOLOv4). Our dataset ( n = 583 images, 7579 pig posture) was annotated in Labelbox from 2D video recordings of groups ( n = 12–15) of weaned pigs. The model recognized each individual pig’s body with a precision of 96% related to threshold intersection over union (IoU), whilst the precision for tails was 77% and for heads this was 66%, thereby already achieving human-level precision. The precision of pig detection in groups was the highest, while head and tail detection precision were lower. As the first study was relatively time-consuming, in the second part of the study, we performed a YOLOv4 neural network analysis using 30 annotated images of our dataset for detecting straight and curled tails. With this model, we were able to recognize tail postures with a high level of precision (90%).

Keywords: pig; welfare; image processing; object detection; deep learning; smart farming (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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