Automated computer vision system to predict body weight and average daily gain in beef cattle during growing and finishing phases

Cominotte, Alexandre; Fernandes, Arthur Francisco Araújo; Dórea, João Ricardo Rebouças; Rosa, Guilherme J. M.; Ladeira, Márcio Machado; Cleef, E. H. C. B. Van; Pereira, Guilherme Luís; Baldassini, Welder Angelo; Neto, Otávio Rodrigues Machado

doi:10.1016/j.livsci.2019.103904

Cited by 98 publications

(39 citation statements)

References 22 publications

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“…Still, the use of DXA, in order to estimate the carcass tissue composition in beef cattle, was previously explored ( Lopez-Campos et al, 2018 ; Calnan et al, 2021 ; Segura et al, 2021 ). Recently, interest in the imaging of living beef cattle also increased with the development of in vivo 3D imaging thanks to 3D cameras ( Gomes et al, 2016 ; Miller et al, 2019 ; Cominotte et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Estimation of empty body and carcass chemical composition of lactating and growing cattle: comparison of imaging, adipose cellularity, and rib dissection methods

Xavier

Driesen

Siegenthaler

et al. 2022

Translational Animal Science

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The aim of present study was to compare in vivo and post mortem methods for estimating the empty body (EB) and carcass chemical compositions of Simmental lactating and growing cattle. Indirect methods were calibrated against the direct post mortem reference determination of chemical compositions of EB and carcass, determined after grinding and analyzing the water, lipid, protein, mineral masses, and energy content. The indirect methods applied to 12 lactating cows and 10 of their offspring were ultrasound (US), half-carcass and 11 th rib dual-energy X-ray absorptiometry (DXA) scans, subcutaneous and perirenal adipose cell size (ACS), and dissection of the 11 th rib. Additionally, three-dimensional (3D) images were captured for eight cows. Multiple linear regressions with leave-one-out-cross-validations were tested between predictive variables derived from the methods tested, and the EB and carcass chemical compositions. Partial least square regressions were used to estimate body composition with morphological traits measured on 3D images. Body weight (BW) alone estimated the EB and carcass composition masses with a root mean squared error of prediction (RMSEP) for the EB from 1 kg for minerals to 12.4 kg for lipids, and for carcass from 0.9 kg for minerals to 7.8 kg for water. Subcutaneous adipose tissue thickness measured by US was the most accurate in vivo predictor when associated with BW to estimate chemical composition, with the EB lipid mass RMSEP = 11 kg and R2 = 0.75; carcass water mass RMSEP = 6 kg and R2 = 0.98; and carcass energy content RMSEP = 236 MJ and R2 = 0.91. Post mortem, carcass lipid mass was best estimated by half-carcass DXA scan (RMSEP = 2 kg, R2 = 0.98), 11 th rib DXA scan (RMSEP = 3 kg, R2 = 0.96), 11 th rib dissection (RMSEP = 4 kg, R2 = 0.92), and perirenal ACS (RMSEP = 6 kg, R2 = 0.79) in this respective order. The results obtained by 11 th rib DXA scan were accurate and close to the half-carcass DXA scan with a reduction in scan time. Morphological traits from 3D images delivered promising estimations of the cow EB and carcass chemical component masses with an error less than 13 kg for the EB lipid mass and than 740 MJ for the EB energy. Future research is required to test the 3D imaging method on a larger number of animals to confirm and quantify its interest in estimating body composition in living animals.

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Section: Introductionmentioning

confidence: 99%

Estimation of empty body and carcass chemical composition of lactating and growing cattle: comparison of imaging, adipose cellularity, and rib dissection methods

Xavier

Driesen

Siegenthaler

et al. 2022

Translational Animal Science

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“…Such systems include multiple calibrated 2D cameras ( Taşdemir et al, 2011a , b ), Structure-from-motion systems or hybrid systems that include a combination of 2D cameras and laser projectors such as the Morpho 3D system ( Le Cozler et al, 2019 ). The 3D sensors use a wide variety of technologies, such as time-of-flight cameras, consumer triangulation sensors, and infrared sensors such as Microsoft Kinect ( Kongsro, 2014 ; Gomes et al, 2016 ; Nir et al, 2018 ; Pezzuolo et al, 2018a ; Fernandes et al, 2019 ; Cominotte et al, 2020 ; Martins et al, 2020 ).…”

Section: Methods For Morphometric Measurements Extractionmentioning

confidence: 99%

“… Cominotte et al (2020) assessed the predictive quality of an automated CV system used to predict BW and average daily gain in beef cattle. The authors compared four different predictive approaches, including MLR, least absolute shrinkage and selection operator (LASSO), PLSs, and ANN using 3D images captured with a Kinect model 1473 sensor (Microsoft Corporation, Redmond, WA).…”

Section: And CV Methods For Bw Predictionmentioning

confidence: 99%

ASAS-NANP SYMPOSIUM: Applications of machine learning for livestock body weight prediction from digital images

Wang

Shadpour

Chan

et al. 2021

Journal of Animal Science

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Monitoring, recording, and predicting livestock body weight (BW) allows for timely intervention in diets and health, greater efficiency in genetic selection, and identification of optimal times to market animals because animals that have already reached the point of slaughter represent a burden for the feedlot. There are currently two main approaches (direct and indirect) to measure the BW in livestock. Direct approaches include partial-weight or full-weight industrial scales placed in designated locations on large farms that measure passively or dynamically the weight of livestock. While these devices are very accurate, their acquisition, intended purpose and operation size, repeated calibration and maintenance costs associated with their placement in high-temperature variability, and corrosive environments are significant and beyond the affordability and sustainability limits of small and medium size farms and even of commercial operators. As a more affordable alternative to direct weighing approaches, indirect approaches have been developed based on observed or inferred relationships between biometric and morphometric measurements of livestock and their BW. Initial indirect approaches involved manual measurements of animals using measuring tapes and tubes and the use of regression equations able to correlate such measurements with BW. While such approaches have good BW prediction accuracies, they are time consuming, require trained and skilled farm laborers, and can be stressful for both animals and handlers especially when repeated daily. With the concomitant advancement of contactless electro-optical sensors (e.g., 2D, 3D, infrared cameras), computer vision (CV) technologies, and artificial intelligence fields such as machine learning (ML) and deep learning (DL), 2D and 3D images have started to be used as biometric and morphometric proxies for BW estimations. This manuscript provides a review of CV-based and ML/DL-based BW prediction methods and discusses their strengths, weaknesses, and industry applicability potential.

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“…Constant measurement of traits through high-throughput imaging can reduce management costs, optimize decision-making in livestock operations, and open new possibilities in selective breeding [ 70 ]. The application of such technologies has already been extended to biometric measurements [ 71 ] in beef cattle as well as production traits such as body weight and average daily gain [ 72 ] and body condition score [ 73 ].…”

Section: The Genetics Of Heifers Sexual Precocitymentioning

confidence: 99%

Sustainable Intensification of Beef Production in the Tropics: The Role of Genetically Improving Sexual Precocity of Heifers

Júnior

Silva

Mota

et al. 2022

Animals

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Increasing productivity through continued animal genetic improvement is a crucial part of implementing sustainable livestock intensification programs. In Zebu cattle, the lack of sexual precocity is one of the main obstacles to improving beef production efficiency. Puberty-related traits are complex, but large-scale data sets from different “omics” have provided information on specific genes and biological processes with major effects on the expression of such traits, which can greatly increase animal genetic evaluation. In addition, genetic parameter estimates and genomic predictions involving sexual precocity indicator traits and productive, reproductive, and feed-efficiency related traits highlighted the feasibility and importance of direct selection for anticipating heifer reproductive life. Indeed, the case study of selection for sexual precocity in Nellore breeding programs presented here show that, in 12 years of selection for female early precocity and improved management practices, the phenotypic means of age at first calving showed a strong decreasing trend, changing from nearly 34 to less than 28 months, with a genetic trend of almost −2 days/year. In this period, the percentage of early pregnancy in the herds changed from around 10% to more than 60%, showing that the genetic improvement of heifer’s sexual precocity allows optimizing the productive cycle by reducing the number of unproductive animals in the herd. It has a direct impact on sustainability by better use of resources. Genomic selection breeding programs accounting for genotype by environment interaction represent promising tools for accelerating genetic progress for sexual precocity in tropical beef cattle.

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Automated computer vision system to predict body weight and average daily gain in beef cattle during growing and finishing phases

Cited by 98 publications

References 22 publications

Estimation of empty body and carcass chemical composition of lactating and growing cattle: comparison of imaging, adipose cellularity, and rib dissection methods

Estimation of empty body and carcass chemical composition of lactating and growing cattle: comparison of imaging, adipose cellularity, and rib dissection methods

ASAS-NANP SYMPOSIUM: Applications of machine learning for livestock body weight prediction from digital images

Sustainable Intensification of Beef Production in the Tropics: The Role of Genetically Improving Sexual Precocity of Heifers

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