Estimation of backfat thickness using extracted traits from an automatic 3D optical system in lactating Holstein-Friesian cows

Weber, Astrid; Salau, Jennifer; Haas, Jan; Junge, Wolfgang; Bauer, Ulrike; Harms, Jan; Suhr, Olaf; Schönrock, Karsten; Rothfuss, H.; Bieletzki, Sascha; Thaller, Georg

doi:10.1016/j.livsci.2014.03.022

Cited by 44 publications

(32 citation statements)

References 22 publications

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“…In Weber et al (2014) the distortion of the cow's surface was introduced as a criterion for changes in body condition. Findings on concave and convex areas of the cow's surfaces could thus be used in the analysis of dairy cows' body condition to describe how sunken relevant areas of the animal's body were.…”

Section: Discussionmentioning

confidence: 99%

“…Images of dairy cows' rear views were captured for BCS estimation in Krukowski (2009) with a hand held Time-Of-Flight (TOF) 3D camera while the animals † E-mail: jsalau@tierzucht.uni-kiel.de Animal (2016), 10:9, pp 1513-1524 © The Animal Consortium 2016 doi:10.1017/S1751731116000021 animal 1513 stood still. Additionally, an automated system for backfat thickness (BFT) determination based on a TOF camera mounted in top view position was presented in Salau et al (2014, 1) and Weber et al (2014). The BFT was estimated reaching 0.96 correlation between the backfat thickness measured with a diagnostic ultrasound device and the estimator.…”

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confidence: 99%

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Developing a multi-Kinect-system for monitoring in dairy cows: object recognition and surface analysis using wavelets

Salau

Haas

Thaller

et al. 2016

animal

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Camera-based systems in dairy cattle were intensively studied over the last years. Different from this study, single camera systems with a limited range of applications were presented, mostly using 2D cameras. This study presents current steps in the development of a camera system comprising multiple 3D cameras (six Microsoft Kinect cameras) for monitoring purposes in dairy cows. An early prototype was constructed, and alpha versions of software for recording, synchronizing, sorting and segmenting images and transforming the 3D data in a joint coordinate system have already been implemented. This study introduced the application of two-dimensional wavelet transforms as method for object recognition and surface analyses. The method was explained in detail, and four differently shaped wavelets were tested with respect to their reconstruction error concerning Kinect recorded depth maps from different camera positions. The images' high frequency parts reconstructed from wavelet decompositions using the haar and the biorthogonal 1.5 wavelet were statistically analyzed with regard to the effects of image fore-or background and of cows' or persons' surface. Furthermore, binary classifiers based on the local high frequencies have been implemented to decide whether a pixel belongs to the image foreground and if it was located on a cow or a person. Classifiers distinguishing between image regions showed high (⩾0.8) values of Area Under reciever operation characteristic Curve (AUC). The classifications due to species showed maximal AUC values of 0.69.Keywords: dairy cattle, monitoring system, 3D camera, object recognition, wavelet transform ImplicationThe prototype of a multi-Kinect system is presented that captures 3D data of dairy cows in motion for health monitoring. When fully developed, it will provide an inexpensive means of automated early lameness detection and livestock monitoring, thereby helping to increase animal welfare. The large amount of recorded data will provide a solid basis for scientific analyses and can help to deepen the understanding of connections between traits and genetics. Object recognition and methods to analyze the animals' surfaces from the 3D data are important developmental steps that are approached using the wavelet transform. IntroductionSeveral camera-based studies were conducted over the last years yielding high rates of correct classification in lameness detection in dairy cows. Song et al. (2008), Pluk et al. (2012) and Viazzi et al. (2013) used digital 2D cameras to record walking cows from the side at distances of 3 to 6 m and analyzed step overlap, fetlock joints' angles or back posture. There were also several camera-based studies dealing with body condition score (BCS) determination. Azzaro et al. (2011) used linear and polynomial kernel principal analysis to reconstruct cows' shapes. Bercovich et al. (2012) presented BCS-prediction models that were based on five anatomical points. In both studies the animals were observed from a top view position using 2D cameras. The definitio...

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Developing a multi-Kinect-system for monitoring in dairy cows: object recognition and surface analysis using wavelets

Salau

Haas

Thaller

et al. 2016

animal

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“…As a solution to the comparability problem 2 P ω was recommended. With regard to the within-subject design of the analyses the formula, 2 P ω = df factor * (MS factor − MS error )/SS factor + (N − df factor ) * MS error (5) was used [23], where df factor , MS factor , and SS factor denote the degrees of freedom, the mean squares, and the sum of squares due to the corresponding factor. MS error and SS error denote mean squares and sum of squares of the error, and N denotes the total number of observations.…”

Section: Statistical Analysis Of Cycle Wise Valuesmentioning

confidence: 99%

How does the Behaviour of Dairy Cows during Recording Affect an Image Processing Based Calculation of the Udder Depth?

Salau¹,

Haas²,

Junge³

et al. 2018

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Precision Livestock Farming studies are based on data that was measured from animals via technical devices. In the means of automation, it is usually not accounted for the animals' reaction towards the devices or individual animal behaviour during the gathering of sensor data. In this study, 14 Holstein-Friesian cows were recorded with a 2D video camera while walking through a scanning passage comprising six Microsoft Kinect 3D cameras. Elementary behavioural traits like how long the cows avoided the passage, the time they needed to walk through or the number of times they stopped walking were assessed from the video footage and analysed with respect to the target variable "udder depth" that was calculated from the recorded 3D data using an automated procedure. Ten repeated passages were recorded of each cow. During the repetitions, the cows adjusted individually (p < 0.001) to the recording situations. The averaged total time to complete a passage (p = 0.05) and the averaged number of stops (p = 0.07) depended on the lactation numbers of the cows. The measurement precision of target variable "udder depth" was affected by the time the cows avoided the recording (p = 0.06) and by the time it took them to walk through the scanning passage (p = 0.03). Effects of animal behaviour during the collection of sensor data can alter the results and should, thus, be considered in the development of sensor based devices.

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“…Recently, consumer depth sensors based on a structured infrared-light(IR) system, such as the Microsoft Kinect (Weber et al, 2015)or the ASUS Xtion Pro, provide 3D data at low cost and have opened new possibilities for acquiring information of livestock conformation. Weber et al have written the software for recording 3D images from TOF camera(SwissRanger SR4000, MesaImaging, Switzerland), taken several cuts along aligned pose of cow through a cow's surface in order to calculate traits that are meaningful to the surface's changes induced by varying body condition during lactation (Weber et al, 2014). But they only observed part body surface of livestock.…”

Section: Related Workmentioning

confidence: 99%

“…As increasing number of studies for shape measurement purposes in livestock farming, many software have been developed in order to acquire 3D surface data of livestock (Kawasue et al, 2013, Weber et al, 2014, Guo et al, 2013, to measure livestock conformation (Weber et al, 2014). However, many of these systems were designed only for scanning part of livestock body and state of art livestock whole body surface scanning systems were hard to deploy under livestock production circumstances.…”

Section: Related Workmentioning

confidence: 99%

3d Scanning of Live Pigs System and Its Application in Body Measurements

Guo

Wang

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The shape of a live pig is an important indicator of its health and value, whether for breeding or for carcass quality. This paper implements a prototype system for live single pig body surface 3d scanning based on two consumer depth cameras, utilizing the 3d point clouds data. These cameras are calibrated in advance to have a common coordinate system. The live 3D point clouds stream of moving single pig is obtained by two Xtion Pro Live sensors from different viewpoints simultaneously. A novel detection method is proposed and applied to automatically detect the frames containing pigs with the correct posture from the point clouds stream, according to the geometric characteristics of pig's shape. The proposed method is incorporated in a hybrid scheme, that serves as the preprocessing step in a body measurements framework for pigs. Experimental results show the portability of our scanning system and effectiveness of our detection method. Furthermore, an updated this point cloud preprocessing software for livestock body measurements can be downloaded freely from https://github.com/LiveStockShapeAnalysis to livestock industry, research community and can be used for monitoring livestock growth status.

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Estimation of backfat thickness using extracted traits from an automatic 3D optical system in lactating Holstein-Friesian cows

Cited by 44 publications

References 22 publications

Developing a multi-Kinect-system for monitoring in dairy cows: object recognition and surface analysis using wavelets

Developing a multi-Kinect-system for monitoring in dairy cows: object recognition and surface analysis using wavelets

How does the Behaviour of Dairy Cows during Recording Affect an Image Processing Based Calculation of the Udder Depth?

3d Scanning of Live Pigs System and Its Application in Body Measurements

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