College of Engineering, Huazhong Agricultural University, Wuhan 430070, China.
Key Laboratory of Agricultural Equipment in Mid-lower Yangtze River, Ministry of Agriculture, Wuhan 430070, China.
Sensors (Basel). 2020 Jul 31;20(15):4282. doi: 10.3390/s20154282.
Internal body temperature is the gold standard for the fever of pigs, however non-contact infrared imaging technology (IRT) can only measure the skin temperature of regions of interest (ROI). Therefore, using IRT to detect the internal body temperature should be based on a correlation model between the ROI temperature and the internal temperature. When heat exchange between the ROI and the surroundings makes the ROI temperature more correlated with the environment, merely depending on the ROI to predict the internal temperature is unreliable. To ensure a high prediction accuracy, this paper investigated the influence of air temperature and humidity on ROI temperature, then built a prediction model incorporating them. The animal test includes 18 swine. IRT was employed to collect the temperatures of the backside, eye, vulva, and ear root ROIs; meanwhile, the air temperature and humidity were recorded. Body temperature prediction models incorporating environmental factors and the ROI temperature were constructed based on Back Propagate Neural Net (BPNN), Random Forest (RF), and Support Vector Regression (SVR). All three models yielded better results regarding the maximum error, minimum error, and mean square error (MSE) when the environmental factors were considered. When environmental factors were incorporated, SVR produced the best outcome, with the maximum error at 0.478 °C, the minimum error at 0.124 °C, and the MSE at 0.159 °C. The result demonstrated the accuracy and applicability of SVR as a prediction model of pigs' internal body temperature.
体内温度是猪发热的金标准，然而，非接触式红外热成像技术（IRT）只能测量感兴趣区域（ROI）的皮肤温度。因此，使用 IRT 来检测体内温度应该基于 ROI 温度与体内温度之间的相关模型。当 ROI 与周围环境之间的热交换使 ROI 温度与环境更相关时，仅依靠 ROI 来预测体内温度是不可靠的。为了确保较高的预测精度，本文研究了空气温度和湿度对 ROI 温度的影响，然后建立了包含这些因素的预测模型。动物试验包括 18 头猪。IRT 用于采集背部、眼睛、外阴和耳根 ROI 的温度；同时，记录空气温度和湿度。基于反向传播神经网络（BPNN）、随机森林（RF）和支持向量回归（SVR）构建了包含环境因素和 ROI 温度的体温预测模型。当考虑环境因素时，所有三个模型在最大误差、最小误差和均方误差（MSE）方面都取得了更好的结果。当包含环境因素时，SVR 产生了最佳结果，最大误差为 0.478°C，最小误差为 0.124°C，MSE 为 0.159°C。结果表明，SVR 作为猪体内体温预测模型具有较高的准确性和适用性。
