Abstract
Simple SummaryIn response to an increasing shift towards automation in the livestock industry, it is important that, as automated systems are developed, they include the capability for automated monitoring of animal health and welfare. As an alternative to current manual methods, this study reports on the development and validation of an algorithm for automated detection and analysis of the eye and cheek regions from thermal infrared images collected automatically from calves. Such algorithms are essential for the integration of infrared thermography (IRT) technology into automated systems to noninvasively monitor calf health and welfare. As the reliance upon automated systems in the livestock industry increases, technologies need to be developed which can be incorporated into these systems to monitor animal health and welfare. Infrared thermography (IRT) is one such technology that has been used for monitoring animal health and welfare and, through automation, has the potential to be integrated into automated systems on-farm. This study reports on an automated system for collecting thermal infrared images of calves and on the development and validation of an algorithm capable of automatically detecting and analysing the eye and cheek regions from those images. Thermal infrared images were collected using an infrared camera integrated into an automated calf feeder. Images were analysed automatically using an algorithm developed to determine the maximum eye and cheek (3 × 3-pixel and 9 × 9-pixel areas) temperatures in a given image. Additionally, the algorithm determined the maximum temperature of the entire image (image maximum temperature). In order to validate the algorithm, a subset of 350 images analysed using the algorithm were also analysed manually. Images analysed using the algorithm were highly correlated with manually analysed images for maximum image (R2 = 1.00), eye (R2 = 0.99), cheek 3 × 3-pixel (R2 = 0.85) and cheek 9 × 9-pixel (R2 = 0.90) temperatures. These findings demonstrate the algorithm to be a suitable method of analysing the eye and cheek regions from thermal infrared images. Validated as a suitable method for automatically detecting and analysing the eye and cheek regions from thermal infrared images, the integration of IRT into automated on-farm systems has the potential to be implemented as an automated method of monitoring calf health and welfare.
Highlights
Over time, the livestock industry has seen a significant change in response to an increasing reliance on automated systems
Infrared thermography (IRT) has been used in previous studies as a method for the early detection of diseases such as bovine viral diarrhea (BVD), bovine respiratory disease (BRD) [22,23,24] and neonatal calf diarrhea (NCD) [1,25] and, as a method for detecting differences in feed efficiency [26,27,28]
The current study provides a demonstration of an automated IRT system where infrared cameras were programmed to collect images automatically as calves fed from automated calf feeders
Summary
The livestock industry has seen a significant change in response to an increasing reliance on automated systems This reliance has largely been driven by a need to reduce labour costs and has resulted in the development of automated systems such as the robotic milking and automated calf feeder systems seen in modern dairy farming systems [1]. This increasing level of automation in the livestock industry [2] has resulted in a less “hands-on” approach to farming, and there are fewer experienced stock people in the industry [3]. The increased sensitivity of the eye was considered to be attributed to the blood flow being closer to the surface and providing a more accurate reflection of core body temperature [5]
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