[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117969-en":3,"doc-seo-117969-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117969,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Modeling Neonatal Piglet Rectal Temperature with Thermography and Machine Learning","Piglet body temperature drops rapidly after birth, and the size of this decline can delay recovery to thermoregulation status, reducing vigor and increasing preweaning risk. This research quantifies rectal temperature changes in wet neonatal piglets during the first day without drying, and develops thermography-based machine learning models to predict rectal temperature over the same period. Thermal images of the ear are paired with gender, initial weight, and environmental variables to compare 14 models against a simple regression; the best model slightly improves prediction accuracy.","MODELING NEONATAL PIGLET RECTAL TEMPERATURE WITH THERMOGRAPHY AND MACHINE LEARNING  \nYijie Xiong1,2,*, Guoming Li3,4, Naomi C. Willard5, Michael Ellis5, Richard S. Gates6,7  \n1  \n2  \n3  \n4  \n5  \n6  \n7  \n*  \nAnimal Science, University of Nebraska-Lincoln, Lincoln, Nebraska, USA.  \nBiological Systems Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USA. Poultry Science, University of Georgia, Athens, Georgia, USA.  \nInstitute for Integrative Precision Agriculture, University of Georgia, Athens, Georgia, USA. Animal Sciences, University of Illinois Urbana-Champaign, Urbana, Illinois, USA.  \nAgricultural and Biosystems Engineering, Iowa State University, Ames, Iowa, USA.  \nAnimal Science, Iowa State University, Ames, Iowa, USA.  \n[Correspondence: yijie.xiong@unl.edu](Correspondence: yijie.xiong@unl.edu)  \nHIGHLIGHTS  \n􀁸 The rectal temperature and maximum ear base temperature were measured for neonatal piglets after birth.  \n􀁸 Piglets’ rectal temperature dropped on average 5.1 °C and reached 33.6 °C 30-min after birth.  \n􀁸 Machine learning algorithms were evaluated to predict piglet rectal temperature using ear temperatures.  \n􀁸 Machine learning model performance was compared to that of a direct regression using maximum ear base temperature.  \n􀁸 The best machine learning model was 0.2°C more accurate than the direct linear regression model.  \nABSTRACT. Piglet body temperature can drop rapidly after birth, and the magnitude of this drop can delay recovery tohomoeothermic 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 bestperforming machine-learning model (the Lasso regressor) produced a standard error of prediction of 1.5°C. Either predic  \ntion model is appropriate, with the direct regression model being more straightforward for field application. Keywords. Computer vision, Farrowing, Precision livestock farming, Pre-wean mortality.  \nP  \nre-weaning mortality of piglets is not only an economic concern but also a welfare issue in commercial swine production. Although many efforts, such as improved management practices, have increased  \nThe authors have paid for open access for this article. This work is licen","cbCaidPKgpMjyyZH","https://ap.wps.com/l/cbCaidPKgpMjyyZH","pdf",1399366,1,12,"English","en",105,"# Highlights\n## Key measurements and trends\n## Modeling objective and evaluation\n## Comparative results\n# Abstract\n## Background and motivation\n## Research objectives\n## Data collection and inputs\n## Model development and comparison\n## Main findings","[{\"question\":\"Why is neonatal piglet rectal temperature important after birth?\",\"answer\":\"Rectal temperature can drop quickly, and a larger decline may delay recovery of thermoregulation and compromise piglet vigor, affecting survival risk.\"},{\"question\":\"What was measured and when in this study?\",\"answer\":\"Rectal temperature and thermal images of the back of the ears were recorded at multiple time points from 0 up to 1440 minutes after birth.\"},{\"question\":\"How were thermography and machine learning used to predict rectal temperature?\",\"answer\":\"Maximum ear base temperature from thermal images, along with piglet gender, initial weight, and environmental variables, were used as inputs for machine learning models and compared with a direct regression baseline.\"}]","Modeling Neonatal Piglet Rectal Temperature with Thermography and Machine Learning | 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is neonatal piglet rectal temperature important after birth?","Question",{"text":75,"@type":76},"Rectal temperature can drop quickly, and a larger decline may delay recovery of thermoregulation and compromise piglet vigor, affecting survival risk.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What was measured and when in this study?",{"text":80,"@type":76},"Rectal temperature and thermal images of the back of the ears were recorded at multiple time points from 0 up to 1440 minutes after birth.",{"name":82,"@type":73,"acceptedAnswer":83},"How were thermography and machine learning used to predict rectal temperature?",{"text":84,"@type":76},"Maximum ear base temperature from thermal images, along with piglet gender, initial weight, and environmental variables, were used as inputs for machine learning models and compared with a direct regression 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