Automatic solution for detection, identification and biomedical monitoring of a cow using remote sensing for optimised treatment of cattle

Beiderman, Yevgeny; Kunin, Mark; Kolberg, Eli; Halachmi, I.; Abramov, Binyamin; Amsalem, Rafael; Zalevsky, Zeev

doi:10.4081/jae.2014.418

Cited by 7 publications

(5 citation statements)

References 22 publications

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“…Newly developed AI cameras may facilitate the deployment of these types of models, such as the FLIR (Firefly DL) for deep learning or Firefly S for machine vision with affordable costs. The potential applications of the ML model developed include assessment of animal welfare due to heat stress [ 43 ], general health monitoring and disease identification [ 63 , 64 , 65 ], detection of respiratory diseases [ 57 ], biomedical monitoring to optimize cattle treatment [ 66 ], drinking behavior [ 67 ] and transport conditions [ 44 ], among others.…”

Section: Discussionmentioning

confidence: 99%

Biometric Physiological Responses from Dairy Cows Measured by Visible Remote Sensing Are Good Predictors of Milk Productivity and Quality through Artificial Intelligence

Fuentes

Viejo

Tongson

et al. 2021

Sensors

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New and emerging technologies, especially those based on non-invasive video and thermal infrared cameras, can be readily tested on robotic milking facilities. In this research, implemented non-invasive computer vision methods to estimate cow’s heart rate, respiration rate, and abrupt movements captured using RGB cameras and machine learning modelling to predict eye temperature, milk production and quality are presented. RGB and infrared thermal videos (IRTV) were acquired from cows using a robotic milking facility. Results from 102 different cows with replicates (n = 150) showed that an artificial neural network (ANN) model using only inputs from RGB cameras presented high accuracy (R = 0.96) in predicting eye temperature (°C), using IRTV as ground truth, daily milk productivity (kg-milk-day−1), cow milk productivity (kg-milk-cow−1), milk fat (%) and milk protein (%) with no signs of overfitting. The ANN model developed was deployed using an independent 132 cow samples obtained on different days, which also rendered high accuracy and was similar to the model development (R = 0.93). This model can be easily applied using affordable RGB camera systems to obtain all the proposed targets, including eye temperature, which can also be used to model animal welfare and biotic/abiotic stress. Furthermore, these models can be readily deployed in conventional dairy farms.

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Section: Discussionmentioning

confidence: 99%

Biometric Physiological Responses from Dairy Cows Measured by Visible Remote Sensing Are Good Predictors of Milk Productivity and Quality through Artificial Intelligence

Fuentes

Viejo

Tongson

et al. 2021

Sensors

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“…However, similar to the previous study, these authors had errors in the data acquired and excluded data from eight calves. To avoid these problems, remote sensing methods have been explored, such as those developed by Beiderman et al (2014), based on an automatic system to assess HR, RR and chewing activity using a tripod holding a PixeLink B741 camera (PixeLink, Rochester, NY, USA) and a Photop D2100 laser connected to a computer. The laser pointed at the neck and stomach of the cow.…”

Section: Cattlementioning

confidence: 99%

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

Fuentes

Viejo

Tongson

et al. 2022

Anim. Health. Res. Rev.

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Livestock welfare assessment helps monitor animal health status to maintain productivity, identify injuries and stress, and avoid deterioration. It has also become an important marketing strategy since it increases consumer pressure for a more humane transformation in animal treatment. Common visual welfare practices by professionals and veterinarians may be subjective and cost-prohibitive, requiring trained personnel. Recent advances in remote sensing, computer vision, and artificial intelligence (AI) have helped developing new and emerging technologies for livestock biometrics to extract key physiological parameters associated with animal welfare. This review discusses the livestock farming digital transformation by describing (i) biometric techniques for health and welfare assessment, (ii) livestock identification for traceability and (iii) machine and deep learning application in livestock to address complex problems. This review also includes a critical assessment of these topics and research done so far, proposing future steps for the deployment of AI models in commercial farms. Most studies focused on model development without applications or deployment for the industry. Furthermore, reported biometric methods, accuracy, and machine learning approaches presented some inconsistencies that hinder validation. Therefore, it is required to develop more efficient, non-contact and reliable methods based on AI to assess livestock health, welfare, and productivity.

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“…In recent years, optical methods for measuring heart rate have received increased interest and technical development (Halachmi et al, 2019). Beiderman et al (2014) proposed a photonic remote sensing system assembled on a robotic platform to measure important biological indicators such as heart beating, breathing and chewing activity. In this research, the algorithm development used image processing and image pattern recognition techniques.…”

Section: Machine Vision (Cameras)mentioning

confidence: 99%

Large-Scale Phenotyping of Livestock Welfare in Commercial Production Systems: A New Frontier in Animal Breeding

et al. 2020

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Genomic breeding programs have been paramount in improving the rates of genetic progress of productive efficiency traits in livestock. Such improvement has been accompanied by the intensification of production systems, use of a wider range of precision technologies in routine management practices, and high-throughput phenotyping. Simultaneously, a greater public awareness of animal welfare has influenced livestock producers to place more emphasis on welfare relative to production traits. Therefore, management practices and breeding technologies in livestock have been developed in recent years to enhance animal welfare. In particular, genomic selection can be used to improve livestock social behavior, resilience to disease and other stress factors, and ease habituation to production system changes. The main requirements for including novel behavioral and welfare traits in genomic breeding schemes are: (1) to identify traits that represent the biological mechanisms of the industry breeding goals; (2) the availability of individual phenotypic records measured on a large number of animals (ideally with genomic information); (3) the derived traits are heritable, biologically meaningful, repeatable, and (ideally) not highly correlated with other traits already included in the selection indexes; and (4) genomic information is available for a large number of individuals (or genetically close individuals) with phenotypic records. In this review, we (1) describe a potential route for development of novel welfare indicator traits (using ideal phenotypes) for both genetic and genomic selection schemes; (2) summarize key indicator variables of livestock behavior and welfare, including a detailed assessment of thermal stress in livestock; (3) describe the primary statistical and bioinformatic methods available for large-scale data analyses of animal welfare; and (4) identify major advancements, challenges, and opportunities to generate high-throughput and large-scale datasets to enable genetic and genomic selection for improved welfare in livestock. A wide variety of novel welfare indicator traits can be derived from information captured by modern technology such as sensors, automatic feeding systems, milking robots, activity monitors, video cameras, and

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Automatic solution for detection, identification and biomedical monitoring of a cow using remote sensing for optimised treatment of cattle

Cited by 7 publications

References 22 publications

Biometric Physiological Responses from Dairy Cows Measured by Visible Remote Sensing Are Good Predictors of Milk Productivity and Quality through Artificial Intelligence

Biometric Physiological Responses from Dairy Cows Measured by Visible Remote Sensing Are Good Predictors of Milk Productivity and Quality through Artificial Intelligence

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

Large-Scale Phenotyping of Livestock Welfare in Commercial Production Systems: A New Frontier in Animal Breeding

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