A Deep Learning Approach-FDNN: Forest Deep Neural Network to Predict Cow’s Parturition Date

Abstract

In this prospective study, we integrate neural network architecture with a supervised random forest feature detector to develop a new model named Forest Deep Neural Network (FDNN) to predict daily and hourly calving time of cattle. To overcome challenges of prediction problems like data sparsity along with unknown correlation structures, we incorporate the benefits of random forest (RF) with a deep neural network (DNN) to predict the daily and hourly calving time of cattle, which is nobody done yet. For this study, we take a total of 45 Holstein-Friesian cows (27 primiparous and 18 multiparous) for collecting physical activities. Using IceQube and HR Tag technologies, we record daily and hourly lying time, the number of stand-ups, ruminating time, the number of steps, and the number of head moves of cattle from 15 days before the actual calving time. Different statistical analysis has been carried out over the daily and hourly-captured data, and we have found that these monitored physical activities change very significantly over time. We have applied five classifiers such as FDNN, DNN, RF, decision tree (DT), and support vector machine (SVM) over the daily and hourly datasets. Hyperparameter optimization has been conducted over the classifiers using Grid Search approach to filter out the optimal parameter configurations. With optimal parameters, our developed model overpowered the other four classifiers in terms of accuracy, sensitivity, specificity, and ROC score (ACC= 98.38, SN=88.19, SP=98.41, and ROC=99 of predicting daily calving time; ACC=97.93, SN=97.40, SP=89.42, and ROC=98 of predicting hourly calving time).