Possibilities for applying machine learning in dairy cattle breeding

Tarjan, Laslo; Šenk, Ivana; Pracner, Doni; Rajkovic, Dusan; Štrbac, Ljuba

doi:10.1109/infoteh51037.2021.9400672

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…Table 4 shows the relation of the works according to the technique used, such as Bayesian networks [46][47][48]63,73,78], Markov chains [22,41,45,49,67,68,74], logistic regression [14,19,[50][51][52]75], differential equations [19,25,31,33,43,[54][55][56]59,60,62,65,69,76,77,79,115], contact networks [19,62,64,70], and machine learning [13,92,93]. The most-used technique is differential equations, while machine learning is presented in five jobs.…”

Section: Contributions Of the Systematic Literature Reviewmentioning

confidence: 99%

“…The most-used techniques in these works are Markov chains [91,92,96,97,108], differential equations [28,110,111], contact networks [9,92,93,95,98], and machine-learning algorithms [93,96,99,101,102,104,105,107,113,115]. Different studies use mathematical models that range from statistics on social media [24] to machine learning [103] and have different applications in the health field.…”

Section: In Human Epidemiological Analysis What Are the Techniques An...mentioning

confidence: 99%

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Systematic Literature Review of Models Used in the Epidemiological Analysis of Bovine Infectious Diseases

et al. 2022

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There are different bovine infectious diseases that show economic losses and social problems in various sectors of the economy. Most of the studies are focused on some diseases (for example, tuberculosis, salmonellosis, and brucellosis), but there are few studies on other diseases which are not officially controlled but also have an impact on the economy. This work is a systematic literature review on models (as a theoretical scheme, generally in mathematical form) used in the epidemiological analysis of bovine infectious diseases in the dairy farming sector. In this systematic literature review, criteria were defined for cattle, models, and infectious diseases to select articles on Scopus, IEEE, Xplorer, and ACM databases. The relations between the found models (model type, function and the proposed objective in each work) and the bovine infectious diseases, and the different techniques used and the works over infectious disease in humans, are presented. The outcomes obtained in this systematic literature review provide the state-of-the-art inputs for research on models for the epidemiological analysis of infectious bovine diseases. As a consequence of these outcomes, this work also presents an approach of EiBeLec, which is an adaptive and predictive system for the bovine ecosystem, combining a prediction model that uses machine-learning techniques and an adaptive model that adapts the information presented to end users.

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Section: Contributions Of the Systematic Literature Reviewmentioning

confidence: 99%

Section: In Human Epidemiological Analysis What Are the Techniques An...mentioning

confidence: 99%

Systematic Literature Review of Models Used in the Epidemiological Analysis of Bovine Infectious Diseases

et al. 2022

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“…In recent years, traditional selection methods, grounded in quantitative genetics, have increasingly incorporated molecular genetics analyses (Štrbac et al, 2023). In addition to conventional statistical evaluation methods, the adoption of machine learning (ML) techniques has gained prominence, facilitating the development of rapid programs and unveiling previously undiscovered relationships (Shahinfar et al, 2012;Tarjan et al, 2021). The Holstein-Friesian breed is the predominant dairy breed within the registered cattle population in Serbia, with the majority located in the AP Vojvodina.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning in Estimating Milk Yield According to the Phenotypic and Pedigree Data of Holstein-Friesian Cattle in Serbia

Tarjan,

Šenk,

Pracner

et al. 2023

Contemporary Agriculture

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Summary This paper presents a deep neural network (DNN) approach designed to estimate the milk yield of Holstein-Friesian cattle. The DNN comprised stacked dense (fully connected) layers, each hidden layer followed by a dropout layer. Various configurations of the DNN were tested, incorporating 2 and 3 hidden layers containing 8 to 54 neurons. The experiment involved testing the DNN with different activation functions such as the sigmoid, tanh, and rectified linear unit (ReLU). The dropout rates ranging from 0 to 0.3 were employed, with the output layer using a linear activation function. The DNN models were trained using the Adam, SGD, and RMSprop optimizers, with the root mean square error serving as the loss metric. The training dataset comprised information from a unique database containing records of dairy cows in the Republic of Serbia, totaling 3,406 cows. The input parameters (a total of 27) for the DNN included breeding and milk yield data from the cow’s mother, as well as the father’s ID, whereas the output parameters (a total of 8) consisted of milk yield parameters (a total of 3) and breeding parameters of the cow (a total of 5). Training iterations were conducted using a batch size of 8 over 500, 1000, and 5000 epochs.

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Can Machine Learning Algorithms Improve Dairy Management?

Roy

Badhan

2023

Lecture Notes in Electrical Engineering

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Possibilities for applying machine learning in dairy cattle breeding

Cited by 3 publications

References 14 publications

Systematic Literature Review of Models Used in the Epidemiological Analysis of Bovine Infectious Diseases

Systematic Literature Review of Models Used in the Epidemiological Analysis of Bovine Infectious Diseases

Application of Machine Learning in Estimating Milk Yield According to the Phenotypic and Pedigree Data of Holstein-Friesian Cattle in Serbia

Can Machine Learning Algorithms Improve Dairy Management?

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