Non-Invasive Cattle Body Temperature Measurement Using Infrared Thermography and Auxiliary Sensors

Abstract

To achieve a sensitive and accurate method in body temperature measurement of cattle, this study explores the uses of infrared thermography (IRT), an anemometer, and a humiture meter as a multiple sensors architecture. The influence of environmental factors on IRT, such as wind speed, ambient temperature, and humidity, was considered. The proposed signal processes removed the IRT frames affected by air flow, and also eliminated the IRT frames affected by random body movement of cattle using the frame difference method. In addition, the proposed calibration method reduced the impact of ambient temperature and humidity on IRT results, thereby increasing the accuracy of IRT temperature. The difference of mean value and standard deviation value between recorded rectal reference temperature and IRT temperature were 0.04 °C and 0.10 °C, respectively, and the proposed system substantially improved the measurement consistency of the IRT temperature and reference on cattle body temperature. Moreover, with a relatively small IRT image sensor, the combination of multiple sensors architecture and proper data processing still achieved good temperature accuracy. The result of the root-mean-square error (RMSE) was 0.74 °C, which is quite close to the accurate result of the IRT measurement.

Clinical Applications and Factors Involved in Validating Thermal Windows Used in Infrared Thermography in Cattle and River Buffalo to Assess Health and Productivity

“…Future research should also focus on the following aspects: developing experimental designs to study environmental factors, specific characteristics of the animals (e.g., breed, sex, age, skin thickness, tissue disposition), and technical elements (e.g., the angle at which thermograms are taken, capture distance, etc.) [ 125 ]. All of these factors can help improve measurements of thermal changes in microcirculation to foment their evaluation during diverse functional processes in animals.…”

Research on Performance Evaluation and Optimization Theory for Thermal Microscope Imaging Systems

“…In the case of distinguishable signals, the higher harmonics are filtered and the first harmonics are maintained. At this time, the signal peak attenuation is 2 π R( f ) = 8 π 2 MTF( f ), MTF( f ) is the system modulation transfer function (2) As a result of time integration, the signal will be sampled and integrated independently once according to the human eye integration time t e (= 0.2s), while the noise is superimposed in the form of the square root, and so the signal-to-noise ratio will be improved by (t e f p ) 1/2 , f p is the frame frequency.…”

“…Infrared imaging technology shows great advantages in target detection and is widely used in agriculture [1], medicine [2], military [3,4], industrial [5][6][7] and other fields [8]. Due to the limitation of the manufacturing process of infrared imaging detectors, the performance of the infrared imaging system needs to be optimized.…”

The livestock farming digital transformation: implementation of new and emerging technologies using artificial intelligence

“…The integrated sensors node was placed on the neck using a collar, with the option to be powered using a solar panel. Furthermore, Wang et al (2021a) developed a noninvasive/contactless sensor system to assess the body temperature of cattle using an infrared thermal camera (AD-HF048; ADE Technology Inc., Taipei, Taiwan), an anemometer (410i; Testo SE & Co., Kilsyth, VIC, Australia), and a humiture sensor (RC-4HA; Elitech Technology, Inc., Milpitas, CA. USA).…”