Prediction of Frozen Semen Doses Production in Dairy Studs using Machine Learning Algorithm

Chandrashekharaiah Jeevan, Sarojini M.K. Karthickeyan, Alagappan Gopinathan, Gopalan K. Tirumurugaan, Subbiah Vairamuthu. Prediction of Frozen Semen Doses Production in Dairy Studs using Machine Learning Algorithm. The Indian Journal of Veterinary Sciences and Biotechnology. 2022; 18(03).

This study aims to develop models for predicting the number of frozen semen doses produced per ejaculate in dairy studs using Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) techniques. Data from 157,532 ejaculates were analyzed, considering variables such as ejaculate volume, ejaculate number, sperm concentration, initial motility, and post-thaw motility. The ANN model (R²=90.66) demonstrated higher accuracy than the MLR model (R²=73.52), with a lower root mean squared error (RMSE) of 33.89 compared to MLR's RMSE of 57.31. ANN modeling proved to be a more effective method for predicting frozen semen dose production.