Artificial Neural Network Approach to Predict Milk Yield of Dairy Farm
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
www.ijsr.net | Open Access | Fully Refereed | Peer Reviewed International Journal

ISSN: 2319-7064

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Research Paper | Computer Science | Sri Lanka | Volume 9 Issue 12, December 2020

Artificial Neural Network Approach to Predict Milk Yield of Dairy Farm

Nayomi Jeewanthi Gamlath

Artificial neural networks have been widely used in various fields for prediction, classification, control system and pattern recognition. In this research, ANN is used to predict the milk yield of the next month based on the meteorological data. In this prediction, 26 input variables are considered. They are number of milking cows, Number of milking cows in each lactation stage (1 to 7), Number of milking cows in each milking month (1 to 13), Number of rainy days, Average Humidity-Day, Average Humidity-Night, Average Temperature and total rainfall amount. Two layered feed forward neural network is used with back propagation algorithm. Properly trained back propagation network tends to give reasonable answers when presented with inputs that they have never seen. Default back propagation algorithm Levenberg – Marquardt (trainlm) and scaled conjugate gradient (trainscg) are used in this work. The result is obtained with 11 neural network modals which are better than with the rest of modals. The best ANN modal is obtained by using seven combined ANN result. It gives 72 % success in prediction with below 10 % error and 94 % success in prediction with between 15 % error.

Keywords: Dairy Farm, Meteorological data, Milk Yield Prediction, Neural Network

Edition: Volume 9 Issue 12, December 2020

Pages: 558 - 562

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How to Cite this Article?

Nayomi Jeewanthi Gamlath, "Artificial Neural Network Approach to Predict Milk Yield of Dairy Farm", International Journal of Science and Research (IJSR), https://www.ijsr.net/search_index_results_paperid.php?id=SR201210165511, Volume 9 Issue 12, December 2020, 558 - 562

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