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Open AccessArticle

Deep Learning and Machine Vision Approaches for Posture Detection of Individual Pigs

Department of Agricultural and Biosystems Engineering, University of Kassel, 37213 Witzenhausen, Germany
School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
Department of Biosystems and Technology, Swedish University of Agricultural Sciences, 23053 Alnarp, Sweden
Department Animal Husbandry, Thuringian State Institute for Agriculture and Rural Development, 07743 Jena, Germany
Author to whom correspondence should be addressed.
Sensors 2019, 19(17), 3738;
Received: 22 July 2019 / Revised: 15 August 2019 / Accepted: 28 August 2019 / Published: 29 August 2019
(This article belongs to the Special Issue Smart Sensing Technologies for Agriculture)
Posture detection targeted towards providing assessments for the monitoring of health and welfare of pigs has been of great interest to researchers from different disciplines. Existing studies applying machine vision techniques are mostly based on methods using three-dimensional imaging systems, or two-dimensional systems with the limitation of monitoring under controlled conditions. Thus, the main goal of this study was to determine whether a two-dimensional imaging system, along with deep learning approaches, could be utilized to detect the standing and lying (belly and side) postures of pigs under commercial farm conditions. Three deep learning-based detector methods, including faster regions with convolutional neural network features (Faster R-CNN), single shot multibox detector (SSD) and region-based fully convolutional network (R-FCN), combined with Inception V2, Residual Network (ResNet) and Inception ResNet V2 feature extractions of RGB images were proposed. Data from different commercial farms were used for training and validation of the proposed models. The experimental results demonstrated that the R-FCN ResNet101 method was able to detect lying and standing postures with higher average precision (AP) of 0.93, 0.95 and 0.92 for standing, lying on side and lying on belly postures, respectively and mean average precision (mAP) of more than 0.93. View Full-Text
Keywords: convolutional neural networks; livestock; lying posture; standing posture convolutional neural networks; livestock; lying posture; standing posture
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Nasirahmadi, A.; Sturm, B.; Edwards, S.; Jeppsson, K.-H.; Olsson, A.-C.; Müller, S.; Hensel, O. Deep Learning and Machine Vision Approaches for Posture Detection of Individual Pigs. Sensors 2019, 19, 3738.

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