Zhang Changzhen, Wu Xiaoping, Xiao Deqin, Zhang Xude, Lei Xiaopeng, Lin Sicong
College of Microelectronics and Artificial Intelligence, Kaili University, Kaili 556011, China.
College of Mathematics Informatics, South China Agricultural University, Guangzhou 510642, China.
Animals (Basel). 2025 Aug 4;15(15):2279. doi: 10.3390/ani15152279.
The goal of this study was to develop an automated monitoring system based on infrared thermography (IRT) for the detection of group-housed pig ears temperature. The aim in the first part of the study was to recognize pigs' ears by using neural network analysis (SwinStar-YOLO). In the second part of the study, the goal was to automatically extract the maximum and average values of the temperature in the ear region using morphological image processing and a temperature matrix. Our dataset (3600 pictures, 10,812 pig ears) was processed using 5-fold cross-validation before training the ear detection model. The model recognized pigs' ears with a precision of 93.74% related to threshold intersection over union (IoU). Correlation analysis between manually extracted and algorithm-derived ear temperatures from 400 pig ear samples showed coefficients of determination (R2) of 0.97 for maximum and 0.88 for average values. This demonstrates that our proposed method is feasible and reliable for automatic pig ear temperature monitoring, serving as a powerful tool for early health warning.
本研究的目标是开发一种基于红外热成像（IRT）的自动监测系统，用于检测群养猪的耳部温度。研究第一部分的目的是使用神经网络分析（SwinStar-YOLO）识别猪的耳朵。在研究的第二部分，目标是使用形态图像处理和温度矩阵自动提取耳部区域温度的最大值和平均值。在训练耳部检测模型之前，我们的数据集（3600张图片，10812只猪耳）采用5折交叉验证进行处理。该模型识别猪耳的精度与交并比（IoU）阈值相关，为93.74%。对400个猪耳样本手动提取的耳部温度和算法得出的耳部温度进行的相关性分析显示，最大值的决定系数（R2）为0.97，平均值的决定系数为0.88。这表明我们提出的方法对于猪耳温度的自动监测是可行且可靠的，可作为早期健康预警的有力工具。
