01  论文基本信息

《智能化农业装备学报（中英文）》2023年第4卷第1期“智能化养殖关键技术与装备”专题刊载了南京农业大学人工智能学院 田浩楠，华婧伊，张少帅，刘龙申的论文——基于红外热成像与线性回归拟合的母猪体温检测技术研究。该研究由江苏省现代农机装备与技术示范推广项目（NJ2020—15）资助。

02  引文信息

引用格式如下，欢迎大家阅读、引用。

田浩楠, 华婧伊, 张少帅, 刘龙申. 基于红外热成像与线性回归拟合的母猪体温检测技术研究[J]. 智能化农业装备学报（中英文）, 2023, 4(1): 36-41.

Tian Haonan, Hua Jingyi, Zhang Shaoshuai, Liu Longshen. Research on the measurement of sow body temperature based on infrared thermography and linear regression fitting[J]. Journal of Intelligent Agricultural Mechanization, 2023, 4(1): 36-41.

DOI: 10.12398/j.issn.2096-7217.2023.01.004

引用格式如下，欢迎大家阅读、引用。

田浩楠, 华婧伊, 张少帅, 刘龙申. 基于红外热成像与线性回归拟合的母猪体温检测技术研究[J]. 智能化农业装备学报（中英文）, 2023, 4(1): 36-41.

Tian Haonan, Hua Jingyi, Zhang Shaoshuai, Liu Longshen. Research on the measurement of sow body temperature based on infrared thermography and linear regression fitting[J]. Journal of Intelligent Agricultural Mechanization, 2023, 4(1): 36-41.

DOI: 10.12398/j.issn.2096-7217.2023.01.004

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03  论文研究内容

摘要：体温是判断猪只健康状况的重要指标之一。为了节省传统猪体温测量所需的人力物力，减小对猪只的应激及人畜交叉感染的风险，本研究利用工业级红外热成像仪（Fluke Ti27）拍摄猪只头部红外热辐射图片。使用深度学习目标检测网络YOLOv3对数据集进行训练预测，实现准确识别定位猪只耳朵所在位置。选取猪只耳根部位作为最佳测量部位，利用Fluke Ti27红外热成像仪配套桌面分析和报告软件 Fluke Connect SmartView获取的热辐射图片中耳根部位温度信息，研究猪只体温与环境温度、环境湿度、光照强度和耳根部位红外温度之间的相关性，建立以猪只体温为因变量，其他变量为自变量的多元线性回归模型，使用多元线性回归函数Regress对猪只体温进行最优拟合。使用该模型对测试集数据进行预估，结果表明：在不同环境条件下，拟合的猪只体温与猪只实际体温的最大误差值为3.06%，平均绝对误差为1.41%，体温拟合较为准确，误差基本满足养猪行业对猪只体温测量误差的要求。该方法可用于养殖生产中猪体温非接触测量，提高了猪只体温测量的精确度及效率，具有较好的前景。

关键词：猪只, 红外热成像, 体温测量, 多元线性回归

Abstract: Body temperature is one of the most important indicators of disease diagnosis in pigs. In order to reduce the manpower and material resources used in the measurement of traditional pigs' temperature methods and decrease the risk of pigs' stress and cross-infection between humans and pigs, the infrared thermal imager (Fluke Ti27) was used to acquire images of sows infrared heat radiation. The deep learning target detection network YOLOv3 was used to train and predict the dataset to accurately identify and locate the ear root of sow. The ear root part of the sow was selected as the best measurement part and with the temperature information of the ear root part in the thermal radiation picture obtained by the Fluke software (Fluke Connect SmartView), the relationships between the sow body temperature and the ambient temperature, the ambient humidity, the light intensity, the infrared temperature of the ear root were analyzed so that the multiple linear regression model with sow body temperature as the dependent variable and other variables as independent variables was established and the multiple linear regression function was used to optimally fit the sow body temperature. Using this model to estimate the data of the test set, the results showed that: under different environmental conditions, the maximum error between the fitted pig body temperature and the actual pig body temperature was 3.06%, and the average absolute error was 1.41%. The body temperature fitting is accurate, and the fitting error basically meets the pig breeding industry requirements. This method can be used as a non-contact measurement of pig body temperature in pig production, which improves the accuracy and efficiency of temperature measurement and has a good prospect.

Keywords: sow, infrared thermography, body temperature measurement, multiple linear regression

04  作者简介

第一作者：田浩楠，内蒙古乌兰察布人；研究方向为畜禽智能养殖技术装备。E-mail: 3116814931@qq.com

通讯作者：刘龙申，河南周口人，博士，副教授；研究方向为畜禽智能养殖技术装备。E-mail: liulongshen@njau.edu.cn

《智能化农业装备学报（中英文）》是经国家新闻出版署批准，中华人民共和国农业农村部主管、农业农村部南京农业机械化研究所主办的农业工程类学术期刊。中国工程院罗锡文院士担任编辑委员会主任委员。国内统一刊号：CN 32-1887/S2国际标准刊号：ISSN 2096-7217

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