Automatic weight estimation of individual pigs using image analysis

Kashiha, Mohammadamin; Bahr, Claudia; Ott, Sanne; Moons, Christel; Niewold, Theodoor; Ödberg, Frank; Berckmans, Daniël

doi:10.1016/j.compag.2014.06.003

Cited by 117 publications

(70 citation statements)

References 25 publications

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“…Moreover, MAPE, RMSE, SRE and RAV respectively calculated: 21.465, 102.97, 0.240 and 0.0578. The R 2 value of broiler weight in this research was delicately better than that reported by Kashiha et al (2014) who estimated pig weight of 97.5% at the group level (error of 0.82 kg) and 96.2% individually (error of 1.23 kg) by computer-assisted digital image analysis. In Amraei et al (2017a) study, Bayesian regulation ANN technique with an R 2 value of 0.98 as the best network was used for prediction of broiler weight and the most errors were less than 50 g. Other research for body weight broiler using support vector regression estimated R 2 = 0.98 as well (Amraei et al, 2017b).…”

Section: Resultscontrasting

confidence: 75%

Development of a Transfer Function for Weight Prediction of Live Broiler Chicken Using Machine Vision

Amraei¹,

Mehdizadeh²,

Nääs

2018

Eng. Agríc.

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The objective of this study was to process digital images to investigate the possibility of broilers body weight estimation based on the dynamic model. For this experiment, 2440 images were recorded by a top-view camera from 30 birds. An ellipse fitting algorithm was applied to localize chickens within the pen, by using a generalized Hough transform. Chickens' head and tail were removed efficiently using the Chan-Vese method. After that, using image processing, six body measures were calculated. Next, they were used to design a Transform Function (TF) model with weight measurements as output. Secondorder dynamic models were used to predict the weight of life broiler chicken, without delay, stable and with the highest R 2 were predominantly selected according to the Young Identification Criterion (YIC) criterion chosen models. It was observed that predicted values rigorously follow the real values. Moreover, the relative body weight errors of chickens in the early days of grow-out was much more than last days. The accuracy of TF for body weight prediction from a comparison between measured (absolute) and predicted total life body weights were estimated for all studied broiler chicken (R 2 =0.98).

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Section: Resultscontrasting

confidence: 75%

Development of a Transfer Function for Weight Prediction of Live Broiler Chicken Using Machine Vision

Amraei¹,

Mehdizadeh²,

Nääs

2018

Eng. Agríc.

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“…A third approach that is receiving increased attention from swine researchers are machine learning and computer vision algorithms (Kamilaris and Prenafeta-Boldú, 2018). Recent applications include monitoring animal behavior (Cross et al, in press;Lao et al, 2016;Nasirahmadi et al, 2017;Shao and Xin, 2008), and weight (Kashiha et al, 2014;Shi et al, 2016;Wongsriworaphon et al, 2015). In this paper, we propose the use of machine learning algorithms to predict internal-body temperature, skin-surface temperature, and hair-coat surface temperature of piglets from environmental variables.…”

Section: Introductionmentioning

confidence: 99%

Machine learning algorithms to predict core, skin, and hair-coat temperatures of piglets

Gorczyca

Milan

Maia

et al. 2018

Computers and Electronics in Agriculture

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A B S T R A C TInternal-body (core) and surface temperatures of livestock are important information that indicate heat stress status and comfort of animals. Previous studies focused on developing mechanistic and empirical models to predict these temperatures. Mechanistic models based on bioenergetics of animals often require parameters that may be difficult to obtain (e.g., thickness of internal tissues). Empirical models, on the other hand, are databased and often assume linear relationships between predictor (e.g., air temperature) and response (e.g., internal-body temperature) variables although, from the theory of bioenergetics, the relationship between the predictor and the response variables is non-linear. One alternative to consider non-linearity is to use machine learning algorithms to predict physiological temperatures. Unlike mechanistic models, machine learning algorithms do not depend on biophysical parameters, and, unlike linear empirical models, machine learning algorithms automatically select the predictor variables and find non-linear functions between predictor and response variables. In this paper, we tested four different machine learning algorithms to predict rectal (T r ), skin-surface (T s ), and hair-coat surface (T h ) temperatures of piglets based on environmental data. From the four algorithms considered, deep neural networks provided the best prediction for T r with an error of 0.36%, gradient boosted machines provided the best prediction for T s with an error of 0.62%, and random forests provided the best predictions for T h with an error of 1.35%. These three algorithms were robust for a wide range of inputs. The fourth algorithm, generalized linear regression, predicted at higher errors and was not robust for a wide range of inputs. This study supports the use of machine learning algorithms (specifically deep neural networks, gradient boosted machines, and random forests) to predict physiological temperature responses of piglets.

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“…Machine vision-based weighing of pigs is a non-intrusive, fast, and relatively accurate approach that could reduce stress on pigs and the stockman during the weighing process. A complete edge contour of a pig individual is the precondition for pig weight estimation using a machine vision method (Kashiha, Bahr, Ott, Moons, Niewold, € Odberg et al, 2014;Wang et al, 2008).…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Multi-object extraction from topview group-housed pig images based on adaptive partitioning and multilevel thresholding segmentation

Guo

Zhu

Jiao

et al. 2015

Biosystems Engineering

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Automatic weight estimation of individual pigs using image analysis

Cited by 117 publications

References 25 publications

Development of a Transfer Function for Weight Prediction of Live Broiler Chicken Using Machine Vision

Development of a Transfer Function for Weight Prediction of Live Broiler Chicken Using Machine Vision

Machine learning algorithms to predict core, skin, and hair-coat temperatures of piglets

Multi-object extraction from topview group-housed pig images based on adaptive partitioning and multilevel thresholding segmentation

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