Study on Non-Invasive Blood Glucose Monitoring Methods for Japanese Black Cattle

This paper proposes a method for non-invasively measuring blood glucose levels in beef cattle. Proper feeding management is necessary to improve productivity in the livestock industry, and blood tests are effective as a means of monitoring the health status of cattle with feed. However, blood tests...

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Bibliographic Details
Main Authors: Natori, Takahiro, Fujii, Kentaro, Kawatsu, Yuka, Nabenishi, Hisashi, Aikawa, Naoyuki
Format: Conference Proceeding
Language:English
Subjects:
Absorption
Blood glucose levels
Cattle
CNN
Convolutional neural networks
Cows
Glucose
Integrated circuit modeling
Near-infrared spectroscopy
No-invasive
Tail
Time measurement
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Summary:This paper proposes a method for non-invasively measuring blood glucose levels in beef cattle. Proper feeding management is necessary to improve productivity in the livestock industry, and blood tests are effective as a means of monitoring the health status of cattle with feed. However, blood tests are very labor intensive because they require holding cattle and specialized technicians to perform them. A noninvasive method for estimating blood glucose levels in humans has been proposed, using absorbance spectra and the artificial neural network (ANN) obtained by irradiating blood vessels with near-infrared light. In this paper, we will examine whether it is possible to classify blood glucose levels using a convolutional neural network (CNN) based on absorbance spectra of blood vessels obtained with a small near-infrared spectrometer. The proposed method had better blood glucose classification performance than ANN, suggesting its effectiveness as a noninvasive blood glucose measurement method.
ISSN:2993-4443
DOI:10.1109/MOCAST61810.2024.10615394