Investigating the Impact of Physiological Aspect on Cow Milk Production Using Artificial Intelligence

Sugiono, Sugiono; Soenoko, Rudy; Riawati, Lely

doi:10.15866/ireme.v11i1.9873

Cited by 7 publications

(8 citation statements)

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“…The use of artificial intelligence techniques is still not widely used for managing the culling although they have been used for other purposes. For instance, Cavero et al [3] developed a fuzzy logic model for mastitis detection; [13,15] are some examples of using neural networks for classification and control of mastitis in cows milked using an automatic milking system; Shainfar et al [10] used fuzzy neural networks to predict breeding values for dairy cattle; Grzesiak et al [6] also used neural networks to predict milk production; Sugiono et al [12] built an adaptive system (BPNN) to predict performance of dairy cattle based on environmental and physical data; and Sitkowska et al [11] used decision trees to predict the increment of the levels of somatic cells in milk. Thus, to our knowledge, Artificial Intelligence techniques and, particularly, decision trees, have not been used to support the decision of culling the herd.…”

Section: Related Workmentioning

confidence: 99%

Using Decision Trees to Extract Patterns for Dairy Culling Management

López-Suárez

Armengol

Calsamiglia

et al. 2018

IFIP Advances in Information and Communication Technology

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The management of a dairy farm involves taking difficult technical and economic decisions such as the replacement of some cows to either maintain or increase the productivity of the farm. However, there is not a standard method supporting the selection procedure of which animals need to be culled. In the present study we used decision trees to develop a model able to classify a cow according to the average herd productivity. This model, obtained from a data base around 98000 cows, predicts the average milk production of the first lactation of a cow based on the monthly milk controls corresponding to the lactation peak. Our goal is to identify poor productive cows during her first lactation in order to make more accurate selections of which cows should be culled.

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Section: Related Workmentioning

confidence: 99%

Using Decision Trees to Extract Patterns for Dairy Culling Management

López-Suárez

Armengol

Calsamiglia

et al. 2018

IFIP Advances in Information and Communication Technology

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“…El análisis comparativo realizado por estos autores permitió identificar que aunque la ANN-MLP obtuvo resultados aceptables, los pronósticos más precisos se obtuvieron mediante la función de ajuste de superficies y la NARX, respectivamente. Sugiono et al (2016) y Sugiono et al (2017), evidenciaron que la aplicación de un algoritmo genético para fortalecer el aprendizaje de un MLP, permitió realizar eficientemente el pronóstico de las producciones lecheras del ganado de la raza Holstein Friesian con un valor de MSE inferior a 0.0035. Dongre et al (2017), demostraron que las producciones lecheras del ganado de la raza Deoni pueden ser pronosticadas eficientemente mediante una ANN de tipo MLP, con valores de R 2 superiores al 89% y de RMSE inferiores a 0.139.…”

Section: Rq4 ¿Qué Tipo De Ann Realiza El Pronóstico De Las Produccion...unclassified

“…La aplicación de modelos de pronóstico en la industria láctea representa un tema de interés para la comunidad científica internacional (Perdigón Llanes & González Benítez, 2020). Sin embargo, su realización constituye una actividad compleja porque son diversos los factores que influyen en la producción de este alimento (Perdigón Llanes & González Benítez, 2020;Sugiono et al, 2017).…”

Section: Introductionunclassified

Redes neuronales artificiales en el pronóstico de la producción de leche bovina

Llanes¹,

Benítez²

2022

Rev. colomb. comput.

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Los pronósticos facilitan la toma de decisiones en granjas productoras de leche y contribuyen a mejorar la cadena productiva de este alimento. En la literatura se identificó que las redes neuronales artificiales poseen un ajuste aceptable al pronóstico de las producciones de leche. Sin embargo, en las fuentes bibliográficas consultadas no se evidenció un consenso sobre el tipo de red neuronal artificial con mejores rendimientos en esta actividad. Esta investigación tiene como objetivo identificar la red neuronal artificial con mayores índices de desempeño en el pronóstico de la producción de leche bovina. Se realizó una revisión de la literatura relacionada con los pronósticos de las producciones de leche mediante el uso de redes neuronales artificiales. Los resultados obtenidos en la literatura analizada evidenciaron que las redes no lineales autorregresivas con variables exógenas y las redes convolucionales poseen los mejores rendimientos en el pronóstico de la producción de leche bovina.

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“…In the end, the neural network was trained with selected weight connection. According to the reference [36], the best BPNN structure is described as follows: one hidden layer, initial neuron = 17, tanh transfer function between input and hidden layer, the linear sigmoid transfer function between the hidden layer and output layer, quick propagation learning algorithm, Static/Dynamic Zoometry Concept to Design Cattle Facilities Using Back Propagation Neural… http://dx.doi.org/10.5772/intechopen.75136…”

Section: Back Propagation Neural Network (Bpnn)mentioning

confidence: 99%

Static/Dynamic Zoometry Concept to Design Cattle Facilities Using Back Propagation Neural Network (BPNN)

Sugiono¹,

Soenoko²,

Lukodono³

2018

Artificial Intelligence - Emerging Trends and Applications

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The dairy cattle productivity is largely dependent on the facility quality and environmental condition. Various researchers had conducted a study in this field, but it is not developing the knowledge of animal dimensions and behaviors correlated with their facility design. Complexities of dynamics zoometry depend on cow behaviors that they are forced to use neural network (NN) approach. Hence, the purpose of this chapter is to create the concept of static and dynamic zoometry to guide the ergonomics facilities design. The research started with study literature on anthropometry, dairy cattle, facility design, and neural network. The following step is collecting the static zoometry data in 16 dimensions and dynamics zoometry in 7 dimensions. On the one hand, static data is utilized as an input factor. On the other hand, dynamic data is utilized as desire factor of back propagation neural network (BPNN) model. The result of BPNN training is utilized to design the dairy cattle facilities, e.g., cage with minimal length = 357.67 cm, width = 132.03 cm (per tail), and height = 205.28 cm. The chapter successfully developed the concept of zoometry approach and BPNN model as a pioneer of implementing comfort knowledge.

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Investigating the Impact of Physiological Aspect on Cow Milk Production Using Artificial Intelligence

Cited by 7 publications

References 0 publications

Using Decision Trees to Extract Patterns for Dairy Culling Management

Using Decision Trees to Extract Patterns for Dairy Culling Management

Redes neuronales artificiales en el pronóstico de la producción de leche bovina

Static/Dynamic Zoometry Concept to Design Cattle Facilities Using Back Propagation Neural Network (BPNN)

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