Development of Lifetime Milk Yield Equation Using Artificial Neural Network in Holstein Friesian Cross Breddairy Cattle and Comparison with Multiple Linear Regression Model

Bhosale, Manisha Dinesh; Singh, T. P.

doi:10.18520/cs/v113/i05/951-955

Cited by 6 publications

(6 citation statements)

References 9 publications

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“…Moreover, ANN was very effective in this case for both smaller (fewer than 10 cows) and larger herds. Considering the results reported by Bhosale and Singh [ 61 ], it should be noted that, in the present study, the predictive abilities of ANN were confirmed based on the first-lactation data, including Wood’s model parameters. Consequently, the effectiveness of ANN in predicting phenotypic milk performance traits is even more important due to the fact that it corresponds to the significant abilities of ANN to predict breeding value of dairy cattle [ 62 ].…”

Section: Discussionsupporting

confidence: 85%

“…For the potential application of ANN in dairy production, prediction of culling reasons in cows should be considered in a broader context, i.e., concerning the lifetime performance of animals. In this regard, Kumar and Hooda [ 60 ] stated that artificial intelligence may be successfully applied to the prediction of lifetime milk yield of cows based on age at first calving, calving interval, and some parameters of the first and second lactation (service period, lactation milk yield, lactation length, and dry period), whereas Bhosale and Singh [ 61 ] reported that, for the effective prediction of lifetime milk yield in cross-bred cows with a proportion of Holstein-Friesian genes, it is sufficient to include only the first-lactation parameters in the ANN input layer (lactation length, peak yield, and lactation total milk yield). Moreover, ANN was very effective in this case for both smaller (fewer than 10 cows) and larger herds.…”

Section: Discussionmentioning

confidence: 99%

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The Use of Artificial Neural Networks and a General Discriminant Analysis for Predicting Culling Reasons in Holstein-Friesian Cows Based on First-Lactation Performance Records

Adamczyk

Grzesiak

Zaborski

2021

Animals

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The aim of the present study was to verify whether artificial neural networks (ANN) may be an effective tool for predicting the culling reasons in cows based on routinely collected first-lactation records. Data on Holstein-Friesian cows culled in Poland between 2017 and 2018 were used in the present study. A general discriminant analysis (GDA) was applied as a reference method for ANN. Considering all predictive performance measures, ANN were the most effective in predicting the culling of cows due to old age (99.76–99.88% of correctly classified cases). In addition, a very high correct classification rate (99.24–99.98%) was obtained for culling the animals due to reproductive problems. It is significant because infertility is one of the conditions that are the most difficult to eliminate in dairy herds. The correct classification rate for individual culling reasons obtained with GDA (0.00–97.63%) was, in general, lower than that for multilayer perceptrons (MLP). The obtained results indicated that, in order to effectively predict the previously mentioned culling reasons, the following first-lactation parameters should be used: calving age, calving difficulty, and the characteristics of the lactation curve based on Wood’s model parameters.

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Section: Discussionsupporting

confidence: 85%

Section: Discussionmentioning

confidence: 99%

The Use of Artificial Neural Networks and a General Discriminant Analysis for Predicting Culling Reasons in Holstein-Friesian Cows Based on First-Lactation Performance Records

Adamczyk

Grzesiak

Zaborski

2021

Animals

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“…Another beneft of the constructed ANN model is its capacity to adapt to new data, if supplied at a later stage, which is unlikely with second-order models. Dongre et al [17], Bhosale, and Singh [19] also reported superior performance of ANN models over multiple regression models for the prediction of milk production.…”

Section: Second-order Model and Performance Comparison With Annmentioning

confidence: 98%

Determining Point of Economic Cattle Milk Production through Machine Learning and Evolutionary Algorithm for Enhancing Food Security

Bhardwaj

Tarafdar

Baghel

et al. 2023

Journal of Food Quality

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Artificial neural networks (ANNs) in conjugation with genetic algorithms (GAs) have been demonstrated to be an effective tool for system modelling and optimization in a variety of applications. The current communique is about assessing the capacity of ANN to predict investment on cattle till age at first calving (AFC) and milk production based on the data of 340 Vrindavani crossbreed cattle developed at the ICAR-Indian Veterinary Research Institute in Izatnagar, India. Three distinct artificial neural network (ANN) algorithms, namely, Levenberg–Marquardt (LM), Bayesian regularization (BR), and gradient descent momentum with adaptive learning rate backpropagation (GDX) were used to train the ANN infrastructure for determining milk production and investment based on body weight and AFC as input variables. The results showed that BR with 2 hidden layer neurons showed excellent prediction ability (R2 = 0.999, MSE < 10−6) and was therefore used as an objective function by GA for optimization. The optimized results revealed that higher milk production is achievable at lower investment if the age at first calving is 768 days with a body weight of ∼281 kg. The information generated by this investigation will aid in ensuring food security in terms of higher milk production while making the dairy business more sustainable and profitable for the farmers.

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“…This study used multiple linear regression (MLR) model (Bhosale and Singh 2015), using multiple weather parameters together. The expression of MLR model can be expressed as:…”

Section: Multiple Linear Regression (Mlr)mentioning

confidence: 99%

Comparative study of different machine learning methods to forecast cucurbit fruit fly, Zeugodacus cucurbitae (Coquillett) occurrence in Eastern plain zone of Uttar Pradesh, India

Deb,

HALDER,

RAI

2024

Preprint

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Insect pests damage a large amount of agricultural produce every year. This damage can be mitigated to a large extent if it is predicted in advance. Machine Learning (ML) models can be a game-changer in terms of pest management policy planning due to their high prediction accuracy. To forecast the occurrence of the cucurbit fruit fly (Zeugodacus cucurbitae), a polyphagous pest in the vegetable ecosystem, different machine learning algorithms such as multiple linear regression, artificial neural networks, support vector machines, decision tree, random forests, and XGBoost were compared. During the years 2013-2019, adults of cucurbit fruit flies were trapped using bottle traps (ethanol, insecticide (Spinosad / Malathion / DDVP) and culture (6:1:2) covered in wooden blocks) at a rate of 25-30 traps per hectare at weekly intervals. The meteorological observatory recorded several weather data such as daily maximum temperature (oC), daily lowest temperature (oC), average daily temperature (oC), average day relative humidity at 7.00 am and 2.00 pm, bright sun shine hour, evaporation, rainfall, and wind velocity. XGBoost has the highest R2, 0.89, out of the six machine learning models tested, followed by the decision tree model (R2= 0.87). As a result, the best fit model for forecasting this cucurbit fruit fly under the vegetable environment might be these two machine learning methods.

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Development of Lifetime Milk Yield Equation Using Artificial Neural Network in Holstein Friesian Cross Breddairy Cattle and Comparison with Multiple Linear Regression Model

Cited by 6 publications

References 9 publications

The Use of Artificial Neural Networks and a General Discriminant Analysis for Predicting Culling Reasons in Holstein-Friesian Cows Based on First-Lactation Performance Records

The Use of Artificial Neural Networks and a General Discriminant Analysis for Predicting Culling Reasons in Holstein-Friesian Cows Based on First-Lactation Performance Records

Determining Point of Economic Cattle Milk Production through Machine Learning and Evolutionary Algorithm for Enhancing Food Security

Comparative study of different machine learning methods to forecast cucurbit fruit fly, Zeugodacus cucurbitae (Coquillett) occurrence in Eastern plain zone of Uttar Pradesh, India

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