پیش‌بینی تولید شیر گاو هلشتاین با استفاده از شبکه‌های عصبی مصنوعی

Adamczyk, K., Grzesiak, W. & Zaborski, D. (2021). 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. Animals, 11: 1-18.

Akilli, A. & Atil, H. (2014). Artificial intelligence technologies in dairy science: fuzzy logic and artificial neural network. Journal of Animal Production, 55(1):39–45.

Chaturvedi, S., Yadav, R.L., Gupta, A.K. & Sharma, A.K. (2013). Life Time Milk Amount Prediction in Dairy Cows using Artificial Neural Networks. International Journal Research and Review, 5: 1-6.

Dongre, V.B., Gandhi, R.S., Singh, A. & Ruhil, A.P. (2012). Comparative efficiency of artificial neural networks and multiple linear regression analysis for prediction of first lactation 305-day milk yield in Sahiwal cattle. Livestock Science, 147: 192-197.

Fernández, C., Soriab, E., Sánchez-Seiquera, P., Gómez-Chovab, L., Magdalenab, R., Martínb, J. D., Navarroc, M. J. & Serrano A. J. (2006). Weekly milk prediction on dairy goats using neural networks. Neural Computing and Applications, 16: 373-381.

Ghotbaldini, H.R., Mohammadabadi, M.R. & Nezamabadi Pour, H. (2016). Using Artificial Intelligence to Estimate the Correction Value of Birth and 3-Month-Old Weights of Kermani Breed Sheep. Modern Genetics, 12: 323-331.

Görgülü, Ö. (2012). Prediction of 305-day milk yield in brown swiss cattle using artificial neural networks. South African Journal of Animal Science, 42(3): 280–7.

Hasani Baferani, A. & Pishkar, J. (2014). Registration of specifications, recording and evaluation of dairy cattle type. Institute of Applied Scientific Education of Jihad Agriculture Press, 260 p. (In Persian).

Izadkhah, R., Farhangfar, H., Fathi Nasri, M. H. & Naeemipour Younesi, H. (2011). Application of Wilmink’s Exponential Function in Genetic Analysis of 305-d Milk Production and Lactation Persistency in Holstein Cows of Razavi Khorasan. Iranian Journal of Animal Science Research, 3: 297-303. (In Persian).

Khazaei, J., Nikosiar, M., Nagatsuka, T. & Ninomiya, S. (2008). Approximating Milk Yield and Milk Fat and Protein Concentration of Cows through the Use of Mathematical and Artificial Neural Networks Models. The World Conference on Agricultural Information and IT, Tokya, Japan, 91- 105.

Khairunniza Bejo, S., Mustaffha, S., Khairunniza-Bejo, S., Ishak, W. & Ismail, W. (2014). Application of Artificial Neural Network in Predicting Crop Yield. Journal of Food Science Enginearing, 4: 1–9.

Kumar, H. & Hooda, B.K. (2014). Prediction of milk production using artificial neural network. Advances in Agriculture and Environmental Science, 6(2):173–5.

Montazer Torbati, M., Moradi Shahr Babak, M., Mirai Ashtiani, R. & Seidenjad, M. (2012). Sustainability Criteria in Holstein Cows of Iran, the First Seminar on Genetics and Breeding of Livestock, Poultry and Fisheries, Tehran, Iran. (In Persian).

Nobari, K., Bane, H., Esmaeilkhanian, S., Yousefi, K. & Samiei, R. (2019). Comparison of linear model and Artificial Neural Network to Prediction of Milk Yield Using First Recorded Parity. Journal of Ruminant Research, 6(4): 89-100. (In Persian).

Pour Hamidi, S., Mohammadabadi, M. R., Asadi Foozi, M. & Nezamabadi-pour, H. (2017). Prediction of breeding values for the milk production trait in Iranian Holstein cows applying artificial neural networks. Journal of Livestock Science and Technologies, 5(2), 53-61.

Radwan, H., El Qaliouby, H. & Elfadl, E. (2020). Classification and prediction of milk yield level for Holstein Friesian cattle using parametric and non-parametric statistical classification models. Journal of Advanced Veterinary and Animal Research, 7(3): 429-435.

Sharma, A., Sharma, R.K. & Kasana, H.S. (2006). Empirical comparisons of feed-forward connectionist and conventional regression models for prediction of first lactation 305-day milk yield in Karan Fries dairy cows. Neural Computing and Applications, 15: 359-365.

Sharma, S. K. & Kumar, S. (2014). Anticipating milk yeild using artificial Neural Network. International Journal of Applied Science and Engineering Research, 3: 690–695.

Streefland, G., Herrema, F. & Martini, M. (2023). A Gradient Boosting model to predict the milk production. Smart Agricultural Technology, 6: 100302.

Tanty, R. & Desmukh, T.S. (2015). Application of Artificial Neural Network in Hydrology-A Review. International Journal of Engineering Research, 4: 184–188.

White, B.W. & Rosenblatt, F. (1963). Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. American Journal of Psychology, 76: 705.