Applications of artificial neural networks for enhanced livestock productivity: A review

Abstract

Artificial neural network models are machine-learning systems, a type of artificial intelligence. They have been inspired by and developed along the working principles of the human brain and its nerve cells. It is used in the modelling of non-linear systems. With the information learned through repeated experience, similar to human learning, artificial neural network can provide classification, pattern recognition, optimisation and the realisation of forward-looking forecasts. Artificial neural network has manifold applications in the field of livestock and allied sections for prediction of milk production, prediction of breeding values of bulls, estrous detection, mastitis prediction and lameness detection, detection of cows with artificial insemination difficulties, prediction of success rate of invitro fertilization, manure nutrient content, volatile fatty acids in the rumen of dairy animals. Artificial neural network models were determined to be more successful than cluster analysis. Most of the published works in data analysis use linear models for forecasting the production parameters; however, sufficient literature proved that by using artificial neural network better results obtained as compared to linear or classical methods. The present manuscript is an attempt to review the systematic information available in livestock and allied sector.