Detection of cows with insemination problems using selected classification models

Grzesiak, Wilhelm; Zaborski, Daniel; Sablik, Piotr; Żukiewicz, Agata; Dybus, Andrzej; Szatkowska, Iwona

doi:10.1016/j.compag.2010.09.001

Cited by 30 publications

(36 citation statements)

References 43 publications

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“…Experimental data sets were randomly divided into training (70%; 1260 observations), validating (15%; 270 observations) and testing (15%; 270 observations) sets. For finding the classification performance, the sensitivity, specificity and accuracy (category-specific and the model's overall performance) were computed based on the following definitions (Grzesiak et al, 2010;Pourreza et al, 2012):…”

Section: Study Area and Animalsmentioning

confidence: 99%

“…The AUC values obtained were 0.98 for the LRST, 0.96 for the ARST and 0.98 for the HRST test sets. The value of AUC represents the discrimination ability of a classifier (Grzesiak et al, 2010) and the value for a realistic classifier should be >0.5, with the AUC range Categorizing pig lying behaviour between 1 (best separation between the values) and 0.5 (no distributional differences between values) (Fawcett, 2006). …”

Section: Lying Patternmentioning

confidence: 99%

“…Some of the ANN applications in recent years have been in livestock-based research: dairy cattle (Grzesiak et al, 2010), sheep (Kominakis et al, 2002;Tahmoorespur and Ahmadi, 2012) and pigs Wongsriworaphon et al, 2015). The performance of classifiers has a significant effect on machine vision outputs (Pourreza et al, 2012), and the feed-forward neural network is one of the most powerful classifiers, which could be fast enough and acceptable for many processes (Khoramshahi et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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A new approach for categorizing pig lying behaviour based on a Delaunay triangulation method

Nasirahmadi

Hensel

Edwards

et al. 2017

Animal

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Machine vision-based monitoring of pig lying behaviour is a fast and non-intrusive approach that could be used to improve animal health and welfare. Four pens with 22 pigs in each were selected at a commercial pig farm and monitored for 15 days using top view cameras. Three thermal categories were selected relative to room setpoint temperature. An image processing technique based on Delaunay triangulation (DT) was utilized. Different lying patterns (close, normal and far) were defined regarding the perimeter of each DT triangle and the percentages of each lying pattern were obtained in each thermal category. A method using a multilayer perceptron (MLP) neural network, to automatically classify group lying behaviour of pigs into three thermal categories, was developed and tested for its feasibility. The DT features (mean value of perimeters, maximum and minimum length of sides of triangles) were calculated as inputs for the MLP classifier. The network was trained, validated and tested and the results revealed that MLP could classify lying features into the three thermal categories with high overall accuracy (95.6%). The technique indicates that a combination of image processing, MLP classification and mathematical modelling can be used as a precise method for quantifying pig lying behaviour in welfare investigations.

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Section: Study Area and Animalsmentioning

confidence: 99%

Section: Lying Patternmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A new approach for categorizing pig lying behaviour based on a Delaunay triangulation method

Nasirahmadi

Hensel

Edwards

et al. 2017

Animal

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“…In animal science, ANNs have been successfully applied to various areas, such as diagnosis of diseases, such as mastitis and lameness (Yang et al, 1999;Cavero et al, 2008;Sun, 2008;Hassan et al, 2009;Roush et al, 2001); the prediction of forward-looking traits (Grzesiak et al, 2003;Salehi et al, 1998;Sanzogni and Kerr, 2001;Kominakis et al, 2002;Hosseinia et al, 2007;Görgülü, 2012); animal breeding studies (Shahinfar et al, 2012;Salehi et al, 1997;Grzesiak et al, 2010); the prediction of the nutrient content in manure (Chen et al, 2008(Chen et al, , 2009; and oestrus detection (Krieter et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Comparison of artificial neural network and K-means for clustering dairy cattle

Atil

Akıllı

2016

IJSAMI

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Abstract:Artificial neural network models (ANN's) are machine-learning systems, a type of artificial intelligence. They have been inspired by and developed along the working principles of the human brain and its nerve cells. ANN's are especially used in the modelling of nonlinear systems. With the information learned through repeated experience, similar to human learning, ANN's can provide classification, pattern recognition, optimisation and the realisation of forward-looking forecasts. Artificial neural network studies have been performed in animal husbandry in recent years. They have been used for the prediction of yield characteristics and classification, animal breeding, quality assessment, and disease diagnosis. In this study, classification of dairy cattle using artificial neural networks and cluster analysis are compared. Artificial neural networks models were determined to be more successful than cluster analysis.

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“…The algorithm produces binary nodes by dividing each node into two child nodes, recursively until homogeneous subgroups are obtained in the tree diagram. With the development of computer technology at the present time, statistical analysis of the algorithm is feasible in SAS (Statistical Analysis System), SPSS (Statistical Package for Social Sciences) (Ali et al, 2015), and STATISTICA (Camdeviren et al, 2007;Nisbet et In the classification problems, usage of various datamining algorithms for animal science has recently been recorded for beef cattle Kucukonder et al, 2015), dairy cattle (Grzesiak et al, 2010;Grzesiak et al, 2011;Zaborski et al, 2014;Bayram et al, 2015), and fisheries science (Topal et al, 2010). Yet, application of data-mining algorithms for poultry science has been scarce for Japanese quail Uckardes et al, 2014) and Chukar partridge (Alectoris chukar) (Karabag et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Usability of CART algorithm for determining egg quality characteristics influencing fertility in the eggs of Japanese quail

et al. 2016

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-The objective of this study was to determine the effects of egg quality characteristics (egg weight, egg width, egg height, and shape index) on fertility of eggs of Japanese quail with different colored feathers (yellow, white, grizzled, and normal), which are of economic importance for poultry production. For this purpose, 383 eggs of Japanese quail with various feather colors were used. In the study, usability of classification and regression tree (CART) data-mining algorithm as a classification tree method is necessary for poultry breeders to define proper cut-off values of egg quality characteristics that ensure Japanese quail eggs at good quality in fertility. Fertility as the dependent variable in the study was examined as a binary trait (fertile and infertile) and all the egg quality traits accepted as explanatory variables were continuous variables. Feather color was also included as a nominal categorical explanatory variable. The classification tree results showed that the highest fertility ratio of 90.9% was obtained from the eggs of Japanese quail of white, grizzled, and normal feather colors with 10.425 g ≥ egg weight, 24.565 mm ≥ egg width, or the fertility ratio of 88.2% of the eggs was observed with 10.425 g ≤ egg weight and 25.605 mm ≥ egg width. Consequently, usability of the tree-based CART algorithm is important in practice for properly establishing fertilized eggs, depending on feather color types of Japanese quail.

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Detection of cows with insemination problems using selected classification models

Cited by 30 publications

References 43 publications

A new approach for categorizing pig lying behaviour based on a Delaunay triangulation method

A new approach for categorizing pig lying behaviour based on a Delaunay triangulation method

Comparison of artificial neural network and K-means for clustering dairy cattle

Usability of CART algorithm for determining egg quality characteristics influencing fertility in the eggs of Japanese quail

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