A Novel Framework to Perform Efficient Analysis of Animal Sciences Using Big Data

Abstract

A crucial trait of the dairy market is the accurate estimate of daily milk demand. Although several models have been using various data analytics over the past decades, but still fails to address this Problems in the Milk Forecast, and also these models were also not used for everyday practice. Milk yield projections of dairy farmers should be made on the basis of each cow and grade. The evaluation of big data is still difficult, considering the growing quantity of milk production information per year. The Prediction and Testing Platform is being introduced to solve dairies' supply chains and to assist dairy farmers. Data analyses are used by producers, in particular small-scale producers, for decision making on their milk availability, to establish a feasible and economical process. Previous work on yield prediction performed with decision tree, KNN classifiers etc. that gives 60-70% accurate results, which is still considered a lagging technology. In our proposed work we made use of logical regression Analysis (LAG) for predicting lactation period and Milk yield for different herd of cow. This method enables dairy farmers to use a range of statistical models to find details on milk production and predicting the potential milk yield at the level of the individual cow and the party. The logistic regression debate on the essence of the project is part of this article. This algorithm gives better prediction result on individual cow's lactation milk yield. This instrument shows how it is possible to use big data analytics in an affordable way.