Feeding behavior recognition for group-housed pigs with the Faster R-CNN

Abstract The feeding behavior of each individual pig is an important indicator to determine whether it is healthy or not. Therefore, automatic behavior recognition for individual pig is one of the core problem in precision pig farming. Video surveillance is a common tool for monitoring animal behaviors. To accurately identify each pig from the video sequences is a prerequisite for individual pig behavior recognition. This paper proposed to use Faster R-CNN to locate and identify individual pigs from a group-housed pen. The head of each pig was also located. An algorithm for associating the head of each pig with its body was designed. On this basis, a behavior recognition algorithm based on feeding area occupation rate was implemented to measure the feeding behavior of pigs. Experiment showed that our algorithm can recognize the feeding behavior of pigs with a precision rate of 99.6% and recall rate of 86.93%.

[1]  Xunmu Zhu,et al.  Automatic recognition of lactating sow postures from depth images by deep learning detector , 2018, Comput. Electron. Agric..

[2]  Felix Adrion,et al.  Monitoring trough visits of growing-finishing pigs with UHF-RFID , 2018, Comput. Electron. Agric..

[3]  Murat Kulahci,et al.  Monitoring pig movement at the slaughterhouse using optical flow and modified angular histograms , 2016 .

[4]  G. Montgomery,et al.  Feeding patterns in pigs: The effects of amino acid deficiency , 1978, Physiology & Behavior.

[5]  Daniel Berckmans,et al.  Automatic Identification of Marked Pigs in a Pen Using Image Pattern Recognition , 2013, MDA.

[6]  Ilias Kyriazakis,et al.  Early detection of health and welfare compromises through automated detection of behavioural changes in pigs , 2016, Veterinary journal.

[7]  Daniel Berckmans,et al.  Analysis of aggressive behaviours of pigs by automatic video recordings , 2013 .

[8]  Roger A. Eigenberg,et al.  Analysis of feeding behavior of group housed growing-finishing pigs , 2013 .

[9]  Uwe Richter,et al.  Using machine vision for investigation of changes in pig group lying patterns , 2015, Comput. Electron. Agric..

[10]  D. Berckmans,et al.  Precision livestock farming technologies for welfare management in intensive livestock systems. , 2014, Revue scientifique et technique.

[11]  Luc Van Gool,et al.  The Pascal Visual Object Classes Challenge: A Retrospective , 2014, International Journal of Computer Vision.

[12]  Weixing Zhu,et al.  Identification of group-housed pigs based on Gabor and Local Binary Pattern features , 2018 .

[13]  Claudia Bahr,et al.  Automatic monitoring of pig locomotion using image analysis , 2014 .

[14]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[15]  Cécile Cornou,et al.  Automatic detection of oestrus and health disorders using data from electronic sow feeders , 2008 .

[16]  Daniel Berckmans,et al.  The use of image analysis as a new approach to assess behaviour classification in a pig barn , 2013 .

[17]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[18]  Daniel Berckmans,et al.  The automatic monitoring of pigs water use by cameras , 2013 .

[19]  Kristof Mertens,et al.  Validation of a High Frequency Radio Frequency Identification (HF RFID) system for registering feeding patterns of growing-finishing pigs , 2014 .

[20]  Weixing Zhu,et al.  Multi-object extraction from topview group-housed pig images based on adaptive partitioning and multilevel thresholding segmentation , 2015 .

[21]  T. M. Brown-Brandl,et al.  Development of a Livestock Feeding Behavior Monitoring System , 2011 .

[22]  Wouter Saeys,et al.  Review: Quantifying animal feeding behaviour with a focus on pigs , 2015, Physiology & Behavior.

[23]  D. Wood‐Gush,et al.  The temporal patterns of food intake and allelomimetic feeding by pigs of different ages , 1984 .

[24]  Weixing Zhu,et al.  Foreground detection of group-housed pigs based on the combination of Mixture of Gaussians using prediction mechanism and threshold segmentation , 2014 .