A study was carried out on 1764 test day records of Deoni cattle breed located in cattle breeding farm, College of Veterinary and Animal Sciences, Udgir Dist- Latur Maharashtra, the western state of India. A multilayer feed forward neural network with back propagation of error learning mechanism was developed using artificial neural network. The network was trained and simulated using Bayesian regularization (BR) algorithms. The performance efficiency of the network model was evaluated using the value of coefficient of determination (R2-value) and root mean square error. It was observed that, the mean value of test days varies from 1.13 to 1.83 with standard deviation ranged from 0.50 to 1.01. Test day nine contributes highest (87.57%) to the total variability. The highest coefficient of determination i.e. 89.55 % was explained in fourth set with 2 layers and 10 neurons. It was inferred that, standard lactation milk yield of Deoni cows could be predicted on the basis of monthly test day milk records as early as 155th days of lactation with 89.55 % of coefficient of determination and 0.138 of root mean squares error.
Artificial Intelligence;Bayesian Regularization;Coefficient of Determination