Research on Seasonal Disease Warning Methods for Northern Winter Sheep Based on Ear-Base Temperature

Area of Science:

• Veterinary Medicine
• Animal Health Monitoring
• Machine Learning in Agriculture

Background:

• Ear-base temperature is a sensitive indicator of sheep core body temperature and health status.
• Winter housing conditions in northern China exacerbate seasonal diseases like pneumonia in sheep, impacting farm economics.
• Current disease detection methods may not provide timely warnings for effective intervention.

Purpose of the Study:

• To develop an early-warning method for detecting winter diseases in sheep using ear-base temperature.
• To establish a predictive model for normal ear-base temperature ranges in healthy sheep.
• To improve the efficiency and economic performance of large-scale sheep farming through timely disease detection.

Main Methods:

• Collected ear temperature, body weight, and environmental data.
• Utilized Random Forest for feature selection and Bayesian optimization for hyperparameter tuning of a 1D convolutional neural network.
• Developed an early-warning strategy based on predicted normal ear-base temperature ranges.

Main Results:

• The proposed method achieved a high detection rate for common winter sheep diseases.
• The system maintained a low false positive rate during experimental validation.
• Effectiveness was confirmed under practical farming conditions.

Conclusions:

• The ear-base temperature monitoring system provides a reliable method for early disease detection in sheep.
• Integration with low-cost temperature-sensing ear tags enables real-time health monitoring.
• The approach enhances management efficiency and economic performance in large-scale sheep farming.