An AI-Driven Multimodal Monitoring System for Early Mastitis Indicators in Italian Mediterranean Buffalo
Mastitis is a significant challenge in the buffalo industry, affecting both milk production and animal health and resulting in economic losses. This study presents the first fully automated AI-driven thermal imaging system integrated with robotic milking, specifically developed for the real-time, no...
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MDPI AG
2025-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/15/4865 |
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| author | Maria Teresa Verde Mattia Fonisto Flora Amato Annalisa Liccardo Roberta Matera Gianluca Neglia Francesco Bonavolontà |
| author_facet | Maria Teresa Verde Mattia Fonisto Flora Amato Annalisa Liccardo Roberta Matera Gianluca Neglia Francesco Bonavolontà |
| author_sort | Maria Teresa Verde |
| collection | DOAJ |
| description | Mastitis is a significant challenge in the buffalo industry, affecting both milk production and animal health and resulting in economic losses. This study presents the first fully automated AI-driven thermal imaging system integrated with robotic milking, specifically developed for the real-time, non-invasive monitoring of udder health in Italian Mediterranean buffalo. Unlike traditional approaches, the system leverages the synchronized acquisition of thermal images during milking and compensates for environmental variables through a calibrated weather station. A transformer-based neural network (SegFormer) segments the udder area, enabling the extraction of maximum udder skin surface temperature (USST), which is significantly correlated with somatic cell count (SCC). Initial trials demonstrate the feasibility of this approach in operational farm environments, paving the way for scalable, precision diagnostics of subclinical mastitis. This work represents a critical step toward intelligent, automated systems for early detection and intervention, improving animal welfare and reducing antibiotic use. |
| format | Article |
| id | doaj-art-d5d51bfc57d944969dd823cdffe2d845 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-d5d51bfc57d944969dd823cdffe2d8452025-08-20T03:36:26ZengMDPI AGSensors1424-82202025-08-012515486510.3390/s25154865An AI-Driven Multimodal Monitoring System for Early Mastitis Indicators in Italian Mediterranean BuffaloMaria Teresa Verde0Mattia Fonisto1Flora Amato2Annalisa Liccardo3Roberta Matera4Gianluca Neglia5Francesco Bonavolontà6Department of Veterinary Medicine and Animal Production, University of Naples Federico II, 80137 Naples, ItalyDepartment of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, ItalyDepartment of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, ItalyDepartment of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, ItalyDepartment of Veterinary Medicine and Animal Production, University of Naples Federico II, 80137 Naples, ItalyDepartment of Veterinary Medicine and Animal Production, University of Naples Federico II, 80137 Naples, ItalyDepartment of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, ItalyMastitis is a significant challenge in the buffalo industry, affecting both milk production and animal health and resulting in economic losses. This study presents the first fully automated AI-driven thermal imaging system integrated with robotic milking, specifically developed for the real-time, non-invasive monitoring of udder health in Italian Mediterranean buffalo. Unlike traditional approaches, the system leverages the synchronized acquisition of thermal images during milking and compensates for environmental variables through a calibrated weather station. A transformer-based neural network (SegFormer) segments the udder area, enabling the extraction of maximum udder skin surface temperature (USST), which is significantly correlated with somatic cell count (SCC). Initial trials demonstrate the feasibility of this approach in operational farm environments, paving the way for scalable, precision diagnostics of subclinical mastitis. This work represents a critical step toward intelligent, automated systems for early detection and intervention, improving animal welfare and reducing antibiotic use.https://www.mdpi.com/1424-8220/25/15/4865infrared thermographyudder healthmachine learninginstrument and measurementsartificial intelligence (AI)early disease detection |
| spellingShingle | Maria Teresa Verde Mattia Fonisto Flora Amato Annalisa Liccardo Roberta Matera Gianluca Neglia Francesco Bonavolontà An AI-Driven Multimodal Monitoring System for Early Mastitis Indicators in Italian Mediterranean Buffalo Sensors infrared thermography udder health machine learning instrument and measurements artificial intelligence (AI) early disease detection |
| title | An AI-Driven Multimodal Monitoring System for Early Mastitis Indicators in Italian Mediterranean Buffalo |
| title_full | An AI-Driven Multimodal Monitoring System for Early Mastitis Indicators in Italian Mediterranean Buffalo |
| title_fullStr | An AI-Driven Multimodal Monitoring System for Early Mastitis Indicators in Italian Mediterranean Buffalo |
| title_full_unstemmed | An AI-Driven Multimodal Monitoring System for Early Mastitis Indicators in Italian Mediterranean Buffalo |
| title_short | An AI-Driven Multimodal Monitoring System for Early Mastitis Indicators in Italian Mediterranean Buffalo |
| title_sort | ai driven multimodal monitoring system for early mastitis indicators in italian mediterranean buffalo |
| topic | infrared thermography udder health machine learning instrument and measurements artificial intelligence (AI) early disease detection |
| url | https://www.mdpi.com/1424-8220/25/15/4865 |
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