Prediction of Health Status of Small-Tailed Cold Sheep Based on Improved BP Neural Network

Abstract

According to related research, different body temperatures, heart rates, and locomotor behaviors of small-tailed cold goats can represent the physical condition of the goats themselves and are used as direct evidence for evaluating the physical health status of small-tailed cold goats. In this paper, we designed and tested a system for predicting the health status of small-tailed cold sheep based on wearable information monitoring technology. To test the system, sheep wearable devices were worn on 36 small-tailed cold sheep of different ages and inconsistent health conditions at different time points from May to October. A SLBAS-BP neural network model for predicting the health condition of small-tailed cold sheep was established using the collected and processed data, which overcame the problem that the traditional gradient descent method in the BP neural network is prone to fall into local optimization leading to insufficient prediction ability. The correct prediction rates of the improved BP neural network for the four health conditions of healthy, sub-healthy, fever, and disease were 98.4%, 94.5%, 90.4%, and 98.7%, respectively, and the average correct prediction rate of the four conditions was 5.8% higher than that before the improvement, reaching 95.2%.