Estimating Conformational Traits in Dairy Cattle With DeepAPS: A Two-Step Deep Learning Automated Phenotyping and Segmentation Approach

Nye, Jessica; Zingaretti, Laura M.; Pérez‐Enciso, Miguel

doi:10.3389/fgene.2020.00513

Cited by 21 publications

(12 citation statements)

References 44 publications

(58 reference statements)

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“…With the advent of phenomics, these traditional phenotypes can be collected automatically on a continuous basis on a massive number of animals, without the need to disturb them. For instance, weight can be accurately measured using 3D imaging [17], or conformation features can be measured from images [26]. These precision livestock farming devices record under normal production conditions of market animals, and not only on elite animals in nucleus farms.…”

Section: Traditional Phenotypes Revisitedmentioning

confidence: 99%

“…to measure similarities between images should be adapted to livestock, and new metrics for videos should be implemented as they are required to compare animal behaviors. Automatic conformation measures [26] must be improved to be performed in vivo with minimal human intervention. A further challenge is to incorporate all this information into genome predictions.…”

Section: The Way Aheadmentioning

confidence: 99%

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Phenomes: the current frontier in animal breeding

Pérez‐Enciso

Steibel

2021

Genet Sel Evol

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Improvements in genomic technologies have outpaced the most optimistic predictions, allowing industry-scale application of genomic selection. However, only marginal gains in genetic prediction accuracy can now be expected by increasing marker density up to sequence, unless causative mutations are identified. We argue that some of the most scientifically disrupting and industry-relevant challenges relate to ‘phenomics’ instead of ‘genomics’. Thanks to developments in sensor technology and artificial intelligence, there is a wide range of analytical tools that are already available and many more will be developed. We can now address some of the pressing societal demands on the industry, such as animal welfare concerns or efficiency in the use of resources. From the statistical and computational point of view, phenomics raises two important issues that require further work: penalization and dimension reduction. This will be complicated by the inherent heterogeneity and ‘missingness’ of the data. Overall, we can expect that precision livestock technologies will make it possible to collect hundreds of traits on a continuous basis from large numbers of animals. Perhaps the main revolution will come from redesigning animal breeding schemes to explicitly allow for high-dimensional phenomics. In the meantime, phenomics data will definitely enlighten our knowledge on the biological basis of phenotypes.

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Section: Traditional Phenotypes Revisitedmentioning

confidence: 99%

Section: The Way Aheadmentioning

confidence: 99%

Phenomes: the current frontier in animal breeding

Pérez‐Enciso

Steibel

2021

Genet Sel Evol

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“…The authors concluded that by leveraging the information from the CVS it was possible to yield more accurate genetic parameters due to the higher volume of data. In another study, Nye et al (74) developed a web scraper and an image segmentation algorithm to extract images and information from breeding programs catalogs. The information retrieved was used in a subsequent step to predict genetic parameters related to coat pigmentation and conformation traits in dairy cattle.…”

Section: Perspectives Of Cvs For High-throughput Phenotypingmentioning

confidence: 99%

Image Analysis and Computer Vision Applications in Animal Sciences: An Overview

2020

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Computer Vision, Digital Image Processing, and Digital Image Analysis can be viewed as an amalgam of terms that very often are used to describe similar processes. Most of this confusion arises because these are interconnected fields that emerged with the development of digital image acquisition. Thus, there is a need to understand the connection between these fields, how a digital image is formed, and the differences regarding the many sensors available, each best suited for different applications. From the advent of the charge-coupled devices demarking the birth of digital imaging, the field has advanced quite fast. Sensors have evolved from grayscale to color with increasingly higher resolution and better performance. Also, many other sensors have appeared, such as infrared cameras, stereo imaging, time of flight sensors, satellite, and hyperspectral imaging. There are also images generated by other signals, such as sound (ultrasound scanners and sonars) and radiation (standard x-ray and computed tomography), which are widely used to produce medical images. In animal and veterinary sciences, these sensors have been used in many applications, mostly under experimental conditions and with just some applications yet developed on commercial farms. Such applications can range from the assessment of beef cuts composition to live animal identification, tracking, behavior monitoring, and measurement of phenotypes of interest, such as body weight, condition score, and gait. Computer vision systems (CVS) have the potential to be used in precision livestock farming and high-throughput phenotyping applications. We believe that the constant measurement of traits through CVS can reduce management costs and optimize decision-making in livestock operations, in addition to opening new possibilities in selective breeding. Applications of CSV are currently a growing research area and there are already commercial products available. However, there are still challenges that demand research for the successful development of autonomous solutions capable of delivering critical information. This review intends to present significant developments that have been made in CVS applications in animal and veterinary sciences and to highlight areas in which further research is still needed before full deployment of CVS in breeding programs and commercial farms.

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“…In beef cattle, heritability and genetic correlations were estimated using image analysis for carcass traits 11 . Also, genetic parameters for coat color and conformational traits have been estimated in dairy cattle using images from web catalogs 12 .…”

Section: Introductionmentioning

confidence: 99%

Estimating genetics of body dimensions and activity levels in pigs using automated pose estimation

Gorssen

Winters

Meyermans

et al. 2022

Sci Rep

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Pig breeding is changing rapidly due to technological progress and socio-ecological factors. New precision livestock farming technologies such as computer vision systems are crucial for automated phenotyping on a large scale for novel traits, as pigs’ robustness and behavior are gaining importance in breeding goals. However, individual identification, data processing and the availability of adequate (open source) software currently pose the main hurdles. The overall goal of this study was to expand pig weighing with automated measurements of body dimensions and activity levels using an automated video-analytic system: DeepLabCut. Furthermore, these data were coupled with pedigree information to estimate genetic parameters for breeding programs. We analyzed 7428 recordings over the fattening period of 1556 finishing pigs (Piétrain sire x crossbred dam) with two-week intervals between recordings on the same pig. We were able to accurately estimate relevant body parts with an average tracking error of 3.3 cm. Body metrics extracted from video images were highly heritable (61–74%) and significantly genetically correlated with average daily gain (rg = 0.81–0.92). Activity traits were low to moderately heritable (22–35%) and showed low genetic correlations with production traits and physical abnormalities. We demonstrated a simple and cost-efficient method to extract body dimension parameters and activity traits. These traits were estimated to be heritable, and hence, can be selected on. These findings are valuable for (pig) breeding organizations, as they offer a method to automatically phenotype new production and behavioral traits on an individual level.

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Estimating Conformational Traits in Dairy Cattle With DeepAPS: A Two-Step Deep Learning Automated Phenotyping and Segmentation Approach

Cited by 21 publications

References 44 publications

Phenomes: the current frontier in animal breeding

Phenomes: the current frontier in animal breeding

Image Analysis and Computer Vision Applications in Animal Sciences: An Overview

Estimating genetics of body dimensions and activity levels in pigs using automated pose estimation

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