Machine learning techniques associated with infrared thermography to optimize the diagnosis of bovine subclinical mastitis.

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dc.contributorRAUL COSTA MASCARENHAS SANTANA, CPPSE; EDILSON DA SILVA GUIMARAES, CPPSE; FERNANDO DAVID CARACUSCHANSKI, UNIVERSIDADE ESTADUAL PAULISTA JÚLIO DE MESQUITA FILHO; LARISSA CRISTINA BRASSOLATTI, UNIVERSIDADE ESTADUAL PAULISTA JÚLIO DE MESQUITA FILHO; MARIA LAURA DA SILVA, CENTRO UNIVERSITÁRIO CENTRAL PAULISTA; ALEXANDRE ROSSETTO GARCIA, CPPSE; JOSE RICARDO MACEDO PEZZOPANE, CPPSE; TERESA CRISTINA ALVES, CPPSE; PATRICIA THOLON, CPPSE; MARCOS VEIGA DOS SANTOS, UNIVERSIDADE DE SÃO PAULO; LUIZ FRANCISCO ZAFALON, CPPSE.
dc.creatorSANTANA, R. C. M.
dc.creatorGUIMARAES, E. da S.
dc.creatorCARACUSCHANSKI, F. D.
dc.creatorBRASSOLATTI, L. C.
dc.creatorSILVA, M. L. da
dc.creatorGARCIA, A. R.
dc.creatorPEZZOPANE, J. R. M.
dc.creatorALVES, T. C.
dc.creatorTHOLON, P.
dc.creatorSANTOS, M. V. dos
dc.creatorZAFALON, L. F.
dc.date2025-08-28T12:53:35Z
dc.date2025-08-28T12:53:35Z
dc.date2025-08-28
dc.date2025
dc.date.accessioned2026-07-07T05:34:39Z
dc.descriptionAbstract: Bovine subclinical mastitis (SCM) is the costliest disease for the dairy industry. Technologies aimed at the early diagnosis of this condition, such as infrared thermography (IRT), can be used to generate large amounts of data that provide valuable information when analyzed using learning techniques. The objective of this study was to evaluate and optimize the use of machine learning by applying the Extreme Gradient Boosting (XGBoost) algorithm in the diagnosis of bovine SCM, based on udder thermogram analysis. Over 14 months, a total of 1035 milk samples were collected from 97 dairy cows subjected to an automatic milking system. Somatic cell counts were performed by flow cytometry, and the health status of the mammary gland was determined based on a cutoff of 200,000 cells/mL of milk. The attributes analyzed collectively included air temperature, relative humidity, temperature-humidity index, breed, body temperature, teat dirtiness score, parity, days in milk, mammary gland position, milk yield, electrical conductivity, milk fat, coldest and hottest points in the mammary gland region of interest, average mammary gland temperature, thermal amplitude, and the difference between the average temperature of the region of interest and the animal’s body temperature, as well as the microbiological evaluation of the milk. Using the XGBoost algorithm, the most relevant variables for solving the classification problem were identified and selected to construct the final model with the best fit and performance. The best area under the receiver operating characteristic curve (AUC: 0.843) and specificity (Sp: 93.3%) were obtained when using all thermographic variables. The coldest point in the region of interest was considered the most important for decision making in mastitis diagnosis. The use of XGBoost can enhance the diagnostic capability for SCM when IRT is employed. The developed optimized model can be used as a confirmatory mechanism for SCM.
dc.format14 p.
dc.identifierVeterinary Medicine International, v. 2025, 5585458, 2025.
dc.identifier2042-0048
dc.identifierhttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1178341
dc.identifier.urihttp://hdl.handle.net/123456789/495556
dc.languageeng
dc.rightsopenAccess
dc.subjectExtreme gradient boosting
dc.subjectRobotic milking system
dc.subjectMachine learning
dc.subjectGado Leiteiro
dc.subjectBovino
dc.subjectDiagnostico
dc.subjectRaio Infravermelho
dc.subjectDairy cattle
dc.subjectMilking equipment
dc.subjectMilking machines
dc.subjectDairy cows
dc.subjectSurface temperature
dc.subjectMilk
dc.subjectMastitis
dc.subjectMammary glands
dc.subjectAnimal diseases
dc.subjectDairy farming
dc.titleMachine learning techniques associated with infrared thermography to optimize the diagnosis of bovine subclinical mastitis.
dc.typeArtigo de periódico

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