[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126391-en":3,"doc-seo-126391-105":31,"detail-sidebar-cat-0-en-105":93},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126391,962085571259,"Theodora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine learning-based clinical mastitis detection in dairy cows using milk electrical conductivity and somatic cell count","Bovine mastitis is a prevalent dairy disease that causes major economic losses and requires accurate, robust detection. Traditional electrical conductivity (EC) threshold methods suffer from limited specificity and farm-to-farm variability. Somatic cell count (SCC) is a more reliable biomarker for intramammary inflammation, but SCC sensing can be costly and may still produce imprecise measurements. This study builds a machine learning diagnostic framework combining logistic regression, support vector machines, and feedforward neural networks using EC, SCC, and combined inputs from 93 cows across four farms. Results show SCC-based models outperform EC-based approaches, with SVM reaching 95.6% accuracy and 100% sensitivity and FNN achieving the highest AUC (0.981). Adding EC to SCC may improve robustness when SCC data are limited, while future work will expand datasets across regions and enable real-time deployment with high-precision sensors.","TYPE Original Research PUBLISHED 14 November 2025 DOI 10.3389/fvets.2025.1671186  \nOPEN ACCESS  \nEDITED BY  \nOm P. Dhungyel,  \nThe University of Sydney, Australia  \nREVIEWED BY  \nSultan Ali,  \nUniversity of Agriculture, Pakistan Jake S. Thompson,  \nUniversity of Nottingham, United Kingdom  \n*CORRESPONDENCE  \nJunbo Wang  \n [jbwang@mail.ie.ac.cn](jbwang@mail.ie.ac.cn)[ ](jbwang@mail.ie.ac.cn)Jian Chen  \n [chenjian@mail.ie.ac.cn](chenjian@mail.ie.ac.cn)[ ](chenjian@mail.ie.ac.cn)Xiaoye Huo  \n [huoxy@aircas.ac.cn](huoxy@aircas.ac.cn)[ ](huoxy@aircas.ac.cn)RECEIVED 22 July 2025 ACCEPTED 28 October 2025 PUBLISHED 14 November 2025  \nCITATION  \nPan L, Chen X, Han D, Li N, Chen D, Wang J, Chen J and Huo X (2025) Machine learning-based clinical mastitis detection in dairy cows using milk electrical conductivity and somatic cell count.  \nFront. Vet. Sci. 12:1671186 .  \ndoi: 10.3389/fvets.2025.1671186  \nCOPYRIGHT  \n© 2025 Pan, Chen, Han, Li, Chen, Wang, Chen and Huo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMachine learning-based clinical mastitis detection in dairy cows using milk electrical conductivity and somatic cell count  \nLihong Pan 1,2, Xiao Chen 1,3, Ding Han 1,4, Nan Li 1, Deyong Chen 1,2,3,4, Junbo Wang 1,2,3,4*, Jian Chen 1,2,3,4* and Xiaoye Huo 1,4*  \n1State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China, 2School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences, Beijing, China, 3School of Future Technology, University of Chinese Academy of Sciences, Beijing, China, 4School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China  \nBovine mastitis, a prevalent disease causing substantial economic losses in dairy production, requires accurate and robust detection methods. Traditional thresholdbased approaches using electrical conductivity (EC) are limited by low specificity and farm-specific variability. While somatic cell count (SCC) offers a more reliable biomarker for intramammary inflammation, current SCC sensors often yield imprecise data and are costly to implement, resulting in a lack of accurate, quantitative, and widely applicable models for mastitis monitoring. This study presents an machine learning-based diagnostic framework integrating logistic regression (LR), support vector machines (SVM), and feedforward neural networks (FNN) to evaluate mastitis detection performance with EC, SCC, and their combined inputs. Using data from 93 cows across four dairy farms, we demonstrate that SCC-based models consistently outperform EC-based approaches. The SVM model achieved 95.6% accuracy and 100% sensitivity when utilizing SCC as input feature. The FNN model attained the highest AUC (0 .981), highlighting neural networks’ capability to capture complex patterns. Although the addition of EC to SCC did not improve performance across all metrics, it showed potential to enhance robustness in contexts where accurate SCC data are limited. These findings underscore the diagnostic superiority of SCC and the potential of tailored machine learning solutions in modern dairy production settings. Future work should focus on expanding datasets across multiple regions and integrating high-precision SCC sensors for real-time deployment in automated detection systems.  \nKEYWORDS  \nsomatic cell count, electrical conductivity, mastitis detection, machine learning, neural network, dairy cows  \n1 Introduction  \nMastitis, an inflammation of the mammary gland typically caused b","cbCaippwLNBxEeYh","https://ap.wps.com/l/cbCaippwLNBxEeYh","pdf",1096266,11,1,9,"English","en",105,"# Introduction\n## Mastitis and diagnostic need\n## Indicators for udder health monitoring\n## Limitations of EC threshold methods\n# Methods\n## Dataset and input features\n## Machine learning models\n# Results\n## Model performance with SCC vs EC\n## Effect of combining EC with SCC\n# Discussion\n## Robustness and practical deployment\n# Conclusion and Future Work","[{\"question\":\"Why are EC-based threshold methods limited for mastitis detection?\",\"answer\":\"EC thresholds show low specificity and strong farm-specific variability. Reported EC threshold values are also inconsistent across studies and implementations.\"},{\"question\":\"Which input feature produced the best detection performance?\",\"answer\":\"Somatic cell count (SCC) consistently outperformed electrical conductivity (EC). The SVM model using SCC achieved 95.6% accuracy and 100% sensitivity.\"},{\"question\":\"Did adding EC to SCC improve mastitis detection models?\",\"answer\":\"Combining EC with SCC did not improve performance across all metrics. It showed potential to enhance robustness when accurate SCC data are limited.\"}]","Machine learning-based clinical mastitis detection in dairy cows using milk electrical conductivity and somatic cell count | PDF",1785904815,23,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"machine-learning-based-clinical-mastitis-detection-in-dairy-cows-using-milk-electrical-conductivity-and-somatic-cell-count","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/machine-learning-based-clinical-mastitis-detection-in-dairy-cows-using-milk-electrical-conductivity-and-somatic-cell-count/126391/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-20","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Why are EC-based threshold methods limited for mastitis detection?","Question",{"text":77,"@type":78},"EC thresholds show low specificity and strong farm-specific variability. Reported EC threshold values are also inconsistent across studies and implementations.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which input feature produced the best detection performance?",{"text":82,"@type":78},"Somatic cell count (SCC) consistently outperformed electrical conductivity (EC). The SVM model using SCC achieved 95.6% accuracy and 100% sensitivity.",{"name":84,"@type":75,"acceptedAnswer":85},"Did adding EC to SCC improve mastitis detection models?",{"text":86,"@type":78},"Combining EC with SCC did not improve performance across all metrics. It showed potential to enhance robustness when accurate SCC data are limited.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,117,122,125,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":113,"doc_module":4,"doc_module_name":47,"category_name":114,"show_sort_weight":115,"slug":116},6,"Technology",50,"technology",{"id":118,"doc_module":4,"doc_module_name":47,"category_name":119,"show_sort_weight":120,"slug":121},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":123,"slug":124},30,"research-report",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":108,"slug":139},19,"General","general"]