Modeling Neonatal Piglet Rectal Temperature with Thermography and Machine Learning

IF 1.2 4区 农林科学 Q3 AGRICULTURAL ENGINEERING Journal of the ASABE Pub Date : 2023-01-01 DOI:10.13031/ja.14998
Y. Xiong, Guoming Li, Naomi C Willard, Michael Ellis, R. Gates
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引用次数: 1

Abstract

Highlights The rectal temperature and maximum ear base temperature were measured for neonatal piglets after birth. Piglets’ rectal temperature dropped on average 5.1 °C and reached 33.6 °C 30-min after birth. Machine learning algorithms were evaluated to predict piglet rectal temperature using ear temperatures. Machine learning model performance was compared to that of a direct regression using maximum ear base temperature. The best machine learning model was 0.2°C more accurate than the direct linear regression model. Abstract. Piglet body temperature can drop rapidly after birth, and the magnitude of this drop can delay recovery to homoeothermic status and compromise the vigor of piglets. Understanding piglet body temperature changes provides critical insights into piglet thermal comfort management and preweaning mortality prevention. However, measuring neonatal piglet body temperature at birth is not generally practical in production facilities, and alternative sensing and modeling methods should be explored. The objectives of this research were to (1) quantify the rectal temperature of wet neonatal piglets without any drying treatments across the first day of birth; (2) develop and evaluate thermography and machine learning models to predict piglet rectal temperature within the same period; and (3) compare the machine learning model’s performance with a simple regression model using the piglets’ thermographic information. Rectal temperatures and thermal images of the back of the ears were obtained at 0, 15, 30, 45, 60, 90, 120, 180, 240, and 1440 minutes after birth for 99 neonatal piglets from 9 litters. Maximum ear base temperature extracted from thermal images, piglet gender, initial weight, and environmental variables (room temperature, relative humidity, and wet-bulb temperature) were used as inputs for machine learning model evaluation. A simple regression and fourteen machine learning models were compared for their performance in predicting piglets’ rectal temperature. Piglets dropped an average of 5.1°C in rectal temperature and reached the lowest temperature (33.6 ± 2.2°C) 30 (±15) minutes after birth, demonstrating a significant reduction from their birth rectal temperature (38.7 ± 0.8°C). The maximum ear base temperature had the highest feature importance score (= 0.606) among all input variables for the machine learning model’s development. A direct regression of maximum ear base temperature against measured rectal temperature produced a standard error of prediction of 1.7°C, while the best-performing machine-learning model (the Lasso regressor) produced a standard error of prediction of 1.5°C. Either prediction model is appropriate, with the direct regression model being more straightforward for field application. Keywords: Computer vision, Farrowing, Precision livestock farming, Pre-wean mortality.
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用热成像和机器学习技术模拟新生仔猪直肠温度
本研究测定了新生仔猪出生后的直肠温度和耳底最高温度。仔猪直肠温度在出生后30分钟平均下降5.1℃,达到33.6℃。评估了机器学习算法通过耳朵温度来预测仔猪直肠温度。将机器学习模型的性能与使用最大耳基温度的直接回归进行比较。最佳机器学习模型比直接线性回归模型精度提高0.2°C。摘要仔猪出生后体温会迅速下降,下降幅度会延迟仔猪恢复到等温状态,损害仔猪的活力。了解仔猪体温变化为仔猪热舒适管理和断奶前死亡率预防提供了重要的见解。然而,在生产设施中,测量新生儿仔猪出生时的体温通常是不实际的,应该探索替代的传感和建模方法。本研究的目的是:(1)量化未进行任何干燥处理的湿新生仔猪在出生第一天的直肠温度;(2)开发和评估热成像和机器学习模型,以预测仔猪同期的直肠温度;(3)将机器学习模型与基于仔猪热像图信息的简单回归模型的性能进行比较。对9窝99头新生仔猪在出生后0、15、30、45、60、90、120、180、240和1440分钟的直肠温度和耳后热像图进行了测量。从热图像中提取的最大耳基温度、仔猪性别、初始体重和环境变量(室温、相对湿度和湿球温度)作为机器学习模型评估的输入。比较了简单回归模型和14种机器学习模型预测仔猪直肠温度的性能。仔猪直肠温度平均下降5.1°C,在出生后30(±15)分钟达到最低温度(33.6±2.2°C),与出生时的直肠温度(38.7±0.8°C)相比显著降低。在机器学习模型开发的所有输入变量中,最高耳基温度的特征重要性得分最高(= 0.606)。最高耳基温度与测量的直肠温度的直接回归产生了1.7°C的预测标准误差,而性能最好的机器学习模型(Lasso回归器)产生了1.5°C的预测标准误差。任何一种预测模型都是合适的,直接回归模型对现场应用更直接。关键词:计算机视觉,产仔,精准养殖,断奶前死亡率
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