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

Ivan Ramirez-Morales,Lenin Aguilar,Jhonny Perez,Alejandro Pazos,Enrique Fernandez-Blanco,Daniel Rivero

doi:10.3390/app112210751

Ivan Ramirez-Morales, Lenin Aguilar + Show 4 more

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https://doi.org/10.3390/app112210751

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Abstract

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%.

Highlights

One of the main problems for cattle herds is mastitis, a disease which is behind considerable economic losses in dairy production
While clinical mastitis presents with a variety of symptoms such as swollen udders, warm quarters, fever and dehydration, which could lead to death [5], subclinical mastitis does not show symptoms of inflammation inside the udder
Data preprocessing is required to enhance the quality of the collected data. This improvement could lead to a reduction of white noise or to the calculation of descriptive features that can be used as inputs to the ML system, instead of raw signals, which can be inputs for a machine learning model

Summary

Introduction

One of the main problems for cattle herds is mastitis, a disease which is behind considerable economic losses in dairy production. The annual economic losses associated to mastitis in cattle range between 1.5 and 2.0 billion in the United States alone [1]. While clinical mastitis presents with a variety of symptoms such as swollen udders, warm quarters, fever and dehydration, which could lead to death [5], subclinical mastitis does not show symptoms of inflammation inside the udder. This latter type is the most important because it is responsible for nearly two-thirds of the economic losses in dairy farms [6]

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