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

## TL;DR

This study introduces a new method to detect early signs of winter diseases in sheep using ear-base temperature, helping farmers monitor health in real-time and reduce economic losses.

## Contribution

A novel early-warning method for winter sheep diseases using ear-base temperature and machine learning, validated in real farming conditions.

## Key findings

- Ear-base temperature is a sensitive and reliable early indicator of disease onset in sheep.
- The proposed method achieved high detection rates for winter diseases with low false positives in practical farming settings.
- Combining ear temperature monitoring with low-cost smart tags enables real-time health tracking for large-scale sheep farming.

## Abstract

Monitoring the health of sheep during winter is challenging, especially in northern regions where animals are kept in closed housing and respiratory diseases are common. Changes in body temperature often occur before visible clinical symptoms appear. In this study, we found that the temperature at the base of the ear could be used as an early indicator of disease in sheep. By continuously monitoring ear temperature together with body weight and environmental conditions, we established a method to identify abnormal temperature changes that may signal the early onset of disease. The proposed approach was tested under practical farming conditions and showed a high ability to detect winter diseases while avoiding false alarms. Combined with low-cost smart ear tags, this method provides a practical tool for real-time health monitoring and early disease warning in large-scale sheep farming.

The temperature at the base of the ear is highly correlated with the core body temperature of sheep and responds sensitively to febrile conditions, making it a valuable indicator of sheep health. In northern China, the closed housing environment during winter increases the incidence of seasonal diseases such as upper respiratory infections and pneumonia, which severely affect the economic efficiency of sheep farming. To address this issue, this study proposes an early-warning method for winter diseases in sheep based on ear-base temperature. Ear temperature, body weight, and environmental data were collected, and Random Forest was employed for feature selection. Bayesian optimization was used to fine-tune the hyperparameters of a one-dimensional convolutional neural network to construct a predictive model of ear-base temperature using data from healthy sheep. Based on the predicted normal range, an early-warning strategy was established to detect abnormal temperature patterns associated with disease onset. Experimental results demonstrated that the proposed method achieved a high detection rate for common winter diseases while maintaining a low false positive rate, and validation experiments confirmed its effectiveness under practical farming conditions. Combined with low-cost temperature-sensing ear tags, the proposed approach enables real-time health monitoring and provides timely early warnings for winter diseases in large-scale sheep farming, thereby improving management efficiency and economic performance.