Detection of Bovine Mastitis in Raw Milk, Using a Low-Cost NIR Spectrometer and k-NN Algorithm

Among the bovine diseases, mastitis causes high economic losses in the dairy production system. Nowadays, detection under field conditions is mainly performed by the California Mastitis Test, which is considered the de facto standard. However, this method presents with problems of slowness and the e...

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Autores principales:	Ivan Ramirez-Morales, Lenin Aguilar, Enrique Fernandez-Blanco, Daniel Rivero, Jhonny Perez, Alejandro Pazos
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	dairy health monitoring California Mastitis Test machine learning near infrared reflected spectra Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/2f9ae38265304bacaaa07d2c0779ad36
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spelling	oai:doaj.org-article:2f9ae38265304bacaaa07d2c0779ad362021-11-25T16:37:25ZDetection of Bovine Mastitis in Raw Milk, Using a Low-Cost NIR Spectrometer and k-NN Algorithm10.3390/app1122107512076-3417https://doaj.org/article/2f9ae38265304bacaaa07d2c0779ad362021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10751https://doaj.org/toc/2076-3417Among the bovine diseases, mastitis causes high economic losses in the dairy production system. Nowadays, detection under field conditions is mainly performed by the California Mastitis Test, which is considered the de facto standard. However, this method presents with problems of slowness and the expensiveness of the chemical-reactive process, which is deeply dependent on an expert’s trained eye and, consequently, is highly imprecise. The aim of this work is to propose a new method for bovine mastitis detection under field conditions. The proposed method uses a low-cost, smartphone-connected NIR spectrometer which solves the aforementioned problems of slowness, expert dependency and disposability of the chemical methods. This method uses spectra in combination with two k-Nearest Neighbors models. The first model is used to detect the presence of mastitis while the second model classifies the positive cases into weak and strong. The resulting method was validated by using a leave-one-out technique where the ground truth was obtained by the California Mastitis Test. The detection model achieved an accuracy of 92.4%, while the one classifying the severity showed an accuracy of 95%.Ivan Ramirez-MoralesLenin AguilarEnrique Fernandez-BlancoDaniel RiveroJhonny PerezAlejandro PazosMDPI AGarticledairyhealth monitoringCalifornia Mastitis Testmachine learningnear infrared reflected spectraTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10751, p 10751 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	dairy health monitoring California Mastitis Test machine learning near infrared reflected spectra Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	dairy health monitoring California Mastitis Test machine learning near infrared reflected spectra Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Ivan Ramirez-Morales Lenin Aguilar Enrique Fernandez-Blanco Daniel Rivero Jhonny Perez Alejandro Pazos Detection of Bovine Mastitis in Raw Milk, Using a Low-Cost NIR Spectrometer and k-NN Algorithm
description	Among the bovine diseases, mastitis causes high economic losses in the dairy production system. Nowadays, detection under field conditions is mainly performed by the California Mastitis Test, which is considered the de facto standard. However, this method presents with problems of slowness and the expensiveness of the chemical-reactive process, which is deeply dependent on an expert’s trained eye and, consequently, is highly imprecise. The aim of this work is to propose a new method for bovine mastitis detection under field conditions. The proposed method uses a low-cost, smartphone-connected NIR spectrometer which solves the aforementioned problems of slowness, expert dependency and disposability of the chemical methods. This method uses spectra in combination with two k-Nearest Neighbors models. The first model is used to detect the presence of mastitis while the second model classifies the positive cases into weak and strong. The resulting method was validated by using a leave-one-out technique where the ground truth was obtained by the California Mastitis Test. The detection model achieved an accuracy of 92.4%, while the one classifying the severity showed an accuracy of 95%.
format	article
author	Ivan Ramirez-Morales Lenin Aguilar Enrique Fernandez-Blanco Daniel Rivero Jhonny Perez Alejandro Pazos
author_facet	Ivan Ramirez-Morales Lenin Aguilar Enrique Fernandez-Blanco Daniel Rivero Jhonny Perez Alejandro Pazos
author_sort	Ivan Ramirez-Morales
title	Detection of Bovine Mastitis in Raw Milk, Using a Low-Cost NIR Spectrometer and k-NN Algorithm
title_short	Detection of Bovine Mastitis in Raw Milk, Using a Low-Cost NIR Spectrometer and k-NN Algorithm
title_full	Detection of Bovine Mastitis in Raw Milk, Using a Low-Cost NIR Spectrometer and k-NN Algorithm
title_fullStr	Detection of Bovine Mastitis in Raw Milk, Using a Low-Cost NIR Spectrometer and k-NN Algorithm
title_full_unstemmed	Detection of Bovine Mastitis in Raw Milk, Using a Low-Cost NIR Spectrometer and k-NN Algorithm
title_sort	detection of bovine mastitis in raw milk, using a low-cost nir spectrometer and k-nn algorithm
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/2f9ae38265304bacaaa07d2c0779ad36
work_keys_str_mv	AT ivanramirezmorales detectionofbovinemastitisinrawmilkusingalowcostnirspectrometerandknnalgorithm AT leninaguilar detectionofbovinemastitisinrawmilkusingalowcostnirspectrometerandknnalgorithm AT enriquefernandezblanco detectionofbovinemastitisinrawmilkusingalowcostnirspectrometerandknnalgorithm AT danielrivero detectionofbovinemastitisinrawmilkusingalowcostnirspectrometerandknnalgorithm AT jhonnyperez detectionofbovinemastitisinrawmilkusingalowcostnirspectrometerandknnalgorithm AT alejandropazos detectionofbovinemastitisinrawmilkusingalowcostnirspectrometerandknnalgorithm
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Detection of Bovine Mastitis in Raw Milk, Using a Low-Cost NIR Spectrometer and k-NN Algorithm

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