An Automatic Head Surface Temperature Extraction Method for Top-View Thermal Image with Individual Broiler

Xiong, Xingguo; Lu, Mingzhou; Yang, Wenhan; Duan, Guanghui; Yuan, Qingyan; Shen, Mingxia; Norton, Tomás; Berckmans, Daniël

doi:10.3390/s19235286

Cited by 16 publications

(12 citation statements)

References 26 publications

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“…Physiological parameters such as respiration rate, temperature or heart rate can be monitored in cattle using image or sensorbased technologies at development stages P1 to P2 (Nogami et al, 2013;Stewart et al, 2017;Strutzke et al, 2019) as well as in poultry (Hyun et al, 2007;Xiong et al, 2019), fish (Martos-Sitcha et al, 2019), and sheep (Dos et al, 2018;Fuchs et al, 2019).…”

Section: Technologies In Developmentmentioning

confidence: 99%

Exploring the Potential of Precision Livestock Farming Technologies to Help Address Farm Animal Welfare

2021

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The rise in the demand for animal products due to demographic and dietary changes has exacerbated difficulties in addressing societal concerns related to the environment, human health, and animal welfare. As a response to this challenge, Precision Livestock Farming (PLF) technologies are being developed to monitor animal health and welfare parameters in a continuous and automated way, offering the opportunity to improve productivity and detect health issues at an early stage. However, ethical concerns have been raised regarding their potential to facilitate the management of production systems that are potentially harmful to animal welfare, or to impact the human-animal relationship and farmers' duty of care. Using the Five Domains Model (FDM) as a framework, the aim is to explore the potential of PLF to help address animal welfare and to discuss potential welfare benefits and risks of using such technology. A variety of technologies are identified and classified according to their type [sensors, bolus, image or sound based, Radio Frequency Identification (RFID)], their development stage, the species they apply to, and their potential impact on welfare. While PLF technologies have promising potential to reduce the occurrence of diseases and injuries in livestock farming systems, their current ability to help promote positive welfare states remains limited, as technologies with such potential generally remain at earlier development stages. This is likely due to the lack of evidence related to the validity of positive welfare indicators as well as challenges in technology adoption and development. Finally, the extent to which welfare can be improved will also strongly depend on whether management practices will be adapted to minimize negative consequences and maximize benefits to welfare.

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Section: Technologies In Developmentmentioning

confidence: 99%

Exploring the Potential of Precision Livestock Farming Technologies to Help Address Farm Animal Welfare

2021

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“…One option for implementing the visual monitoring of a turkey flock is the use of computer vision systems, which can provide non-intrusive, non-invasive, and objective assessments [ 16 ]. Hitherto, previous studies have also developed computer vision systems to observe animal welfare-related issues, such as weight [ 17 ], temperature [ 18 ], thermal stress [ 19 ], and/or lameness [ 20 , 21 ]. These studies only include research concerning broiler chickens, whereas, to the best of our knowledge, no computer vision systems for monitoring animal welfare in turkey husbandry exist.…”

Section: Related Workmentioning

confidence: 99%

Learn to Train: Improving Training Data for a Neural Network to Detect Pecking Injuries in Turkeys

Volkmann

Brünger

Stracke

et al. 2021

Animals

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This study aimed to develop a camera-based system using artificial intelligence for automated detection of pecking injuries in turkeys. Videos were recorded and split into individual images for further processing. Using specifically developed software, the injuries visible on these images were marked by humans, and a neural network was trained with these annotations. Due to unacceptable agreement between the annotations of humans and the network, several work steps were initiated to improve the training data. First, a costly work step was used to create high-quality annotations (HQA) for which multiple observers evaluated already annotated injuries. Therefore, each labeled detection had to be validated by three observers before it was saved as “finished”, and for each image, all detections had to be verified three times. Then, a network was trained with these HQA to assist observers in annotating more data. Finally, the benefit of the work step generating HQA was tested, and it was shown that the value of the agreement between the annotations of humans and the network could be doubled. Although the system is not yet capable of ensuring adequate detection of pecking injuries, the study demonstrated the importance of such validation steps in order to obtain good training data.

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“…Resolution of Thermal Images [64] 60 × 80 (0.005 MP) [65,66] 160 × 120 (0.019 MP) [67][68][69][70][71][72][73][74][75][76][77][78][79][80] 320 × 240 (0.077 MP) [63,81,82] 320 × 256 (0.085 MP) [83,84] 336 × 256 (0.086 MP) [85,86] 384 × 288 (0.111 MP) [59][60][61][62][87][88][89][90][91][92][93][94] 640 × 480 (0.307 MP) [95,96] 640 × 512 (0.328 MP)…”

Section: Referencementioning

confidence: 99%

“…For example, Fleuret et al state that segmentation represents the first step in image processing [100]. In contrast, Waqar Akram et al, Shanmugam and Sekaran, and Xiong et al apply segmentation methods after other operations are completed [78,80,101]. Most sources agree in using a combination of multiple methods in IRT image processing.…”

Section: Referencementioning

confidence: 99%

Application of the Machine Vision Technology and Infrared Thermography to the Detection of Hoof Diseases in Dairy Cows: A Review

et al. 2021

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Infrared thermography (IRT) is a noninvasive and safe method of displaying the temperature map of objects that can be used to detect hoof diseases and lameness to reduce significant financial costs and physically stress animals. A qualitative bibliometric method based on the analysis of publications by the authors themselves using sophisticated tools of scientific databases was applied in this work. This review presents the fundamentals of IRT as well as recent developments in IRT detection in dairy science, including preprocessing, segmentation, and classification of objects in IRT images. In addition, recent studies dealing with the detection of hoof diseases and lameness using IRT are reviewed. As a result of this study, select previous studies are confronted in terms of technical aspects of IRT measurements such as emissivity, distance, temperature range, and reflected air temperature. Subsequently, recommendations for future IRT measurements are discussed.

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An Automatic Head Surface Temperature Extraction Method for Top-View Thermal Image with Individual Broiler

Cited by 16 publications

References 26 publications

Exploring the Potential of Precision Livestock Farming Technologies to Help Address Farm Animal Welfare

Exploring the Potential of Precision Livestock Farming Technologies to Help Address Farm Animal Welfare

Learn to Train: Improving Training Data for a Neural Network to Detect Pecking Injuries in Turkeys

Application of the Machine Vision Technology and Infrared Thermography to the Detection of Hoof Diseases in Dairy Cows: A Review

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