Multiple regression and machine learning based methods for carcass traits and saleable meat cuts prediction using non-invasive in vivo measurements in commercial lambs

Alves, Anderson Luís; Pinzón, Andrés Chaparro; Costa, Rebeka Magalhães da; Silva, Michelle Santos da; Vieira, Eloísa Helena Mendes; Mendonça, Ingrid Barbosa de; Viana, Vinícius de Sena Sales; Lôbo, Raimundo Nonato Braga

doi:10.1016/j.smallrumres.2018.12.008

Cited by 22 publications

(21 citation statements)

References 21 publications

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“…Machine learning is applied often nowadays, and has shown to be competitive with logistic regression in previously conducted studies within the animal domain (e.g., Roush et al, 2006; Felipe et al, 2015; Alsahaf et al, 2018; Alves et al, 2019). To confirm these results with our own data, we also applied logistic regression (using h20.glm) to our data.…”

Section: Discussionmentioning

confidence: 99%

“…Machine-learning techniques are able to deal with incomplete data, irrelevant input variables and are less vulnerable for assumptions concerning, for example, (co)linearity and distributions than classical regression techniques (Breiman, 2001; Friedman, 2001). Furthermore, machine-learning techniques proofed to be competitive in various studies in the animals sciences domain in which future performance was predicted using regression or machine-learning techniques (e.g., Roush et al, 2006; Felipe et al, 2015; Alsahaf et al, 2018; Alves et al, 2019). To predict future performance based on the integration of animal and environmental information, sometimes being incomplete and noisy, machine-learning techniques appear to be a valuable and suitable technique.…”

Section: Introductionmentioning

confidence: 99%

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Boosted trees to predict pneumonia, growth, and meat percentage of growing-finishing pigs1

Mollenhorst

Ducro

Greef

et al. 2019

Journal of Animal Science

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In pig production, efficiency is benefiting from uniform growth in pens resulting in single deliveries from a pen of possibly all animals in the targeted weight range. Abnormalities, like pneumonia or aberrant growth, reduce production efficiency as it reduces the uniformity and might cause multiple deliveries per batch and pigs delivered with a low meat yield or outside the targeted weight range. Early identification of pigs prone to develop these abnormalities, for example, at the onset of the growing-finishing phase, would help to prevent heterogeneous pens through management interventions. Data about previous production cycles at the farm combined with data from the piglet’s own history may help in identifying these abnormalities. The aim of this study, therefore, was to predict at the onset of the growing-finishing phase, that is, at 3 mo in advance, deviant pigs at slaughter with a machine-learning technique called boosted trees. The dataset used was extracted from the farm management system of a research center. It contained over 70,000 records of individual pigs born between 2004 and 2016, including information on, for example, offspring, litter size, transfer dates between production stages, their respective locations within the barns, and individual live-weights at several production stages. Results obtained on an independent test set showed that at a 90% specificity rate, the sensitivity was 16% for low meat percentage, 20% for pneumonia and 36% for low lifetime growth rate. For low lifetime growth rate, this meant an almost three times increase in positive predictive value compared to the current situation. From these results, it was concluded that routine performance information available at the onset of the growing-finishing phase combined with data about previous production cycles formed a moderate base to identify pigs prone to develop pneumonia (AUC > 0.60) and a good base to identify pigs prone to develop growth aberrations (AUC > 0.70) during the growing-finishing phase. The mentioned information, however, was not a sufficient base to identify pigs prone to develop low meat percentage (AUC < 0.60). The shown ability to identify growth aberrations and pneumonia can be considered a good first step towards the development of an early warning system for pigs in the growing-finishing phase.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Boosted trees to predict pneumonia, growth, and meat percentage of growing-finishing pigs1

Mollenhorst

Ducro

Greef

et al. 2019

Journal of Animal Science

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“…For each lamb, the following body measurements (BMs) were recorded 24 h prior to slaughter. The BMs were taken as described previously by Bautista-Diaz et al [8]: (1) height at withers (HW), (2) rib depth (RD), (3) body diagonal length (BDL), (4) body length (BL), (5) pelvic girdle length (PGL), (6) rump depth (RuD), (7) rump height (RH), (8) pin bone width (PBW), (9) hook bone width (HBW), (10) abdomen width (AW), (11) girth circumference (GC), and (12) abdomen circumference (AC) ( Figure 1). Flexible fiberglass tape (Truper ® ) and large 65-cm calipers (Haglof ® ) were used to perform the measurements.…”

Section: Body Measurementsmentioning

confidence: 99%

“…Indirect methods include the prediction of carcass and body composition based on parameters easily obtained [1] through ultrasound [4][5][6], computed tomography, dual x-ray absorptiometry (DEXA), digital image analysis or body measurements [6][7][8][9]. Some of these methods, such as computerised tomography, magnetic resonance, and DEXA are limited to developed countries due to the cost of acquiring the necessary equipment and the need for specialised, professionally trained personnel; also, these methods can be time-consuming [3,[9][10][11]. Ultimately, the selected method for predicting carcass traits and body composition should be based on several factors, including the cost, ease of adoption and prediction accuracy regardless of the sex, age, or diet of animals [1].…”

Section: Introductionmentioning

confidence: 99%

“…In vivo body measurements (BMs) may be of greater interest under commercial production conditions because they enable the assessment of carcass traits or the prediction of commercial cuts with few or no additional costs to producers [8,10,12]. Due to their low cost and practicality, BMs have long been used as an indicator of animal type and potential of production [13,14] or as a predictor of BW, body mass index, and carcass traits [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Carcass Traits of Hair Sheep Lambs Using Body Measurements

Bautista-Díaz

Mezo-Solís

Herrera-Camacho

et al. 2020

Animals

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The present study was designed to evaluate the relationship between the body measurements (BMs) and carcass characteristics of hair sheep lambs. Twenty hours before slaughter, the shrunk body weight (SBW) and BMs were recorded. The BMs involved were height at withers (HW), rib depth (RD), body diagonal length (BDL), body length (BL), pelvic girdle length (PGL), rump depth (RuD), rump height (RH), pin-bone width (PBW), hook-bone width (HBW), abdomen width (AW), girth (GC), and abdomen circumference (AC). After slaughter, the carcasses were weighed and chilled for 24 h at 1 °C, and then were split by the dorsal midline. The left-half was dissected into total soft tissues (muscle + fat; TST) and bone (BON), which were weighed separately. The weights of viscera and organs (VIS), internal fat (IF), and offals (OFF—skin, head, feet, tail, and blood) were also recorded. The equations obtained for predicting SBW, HCW, and CCW had an r2 ranging from 0.89 to 0.99, and those for predicting the TST and BON had an r2 ranging from 0.74 to 0.91, demonstrating satisfactory accuracy. Our results indicated that use of BMs could accurately and precisely be used as a useful tool for predicting carcass characteristics of hair sheep lambs.

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Predicting carcass tissue composition in Blackbelly sheep using ultrasound measurements and machine learning methods

Camacho-Pérez,

Lugo-Quintal,

Tirink

et al. 2023

Trop Anim Health Prod

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Multiple regression and machine learning based methods for carcass traits and saleable meat cuts prediction using non-invasive in vivo measurements in commercial lambs

Cited by 22 publications

References 21 publications

Boosted trees to predict pneumonia, growth, and meat percentage of growing-finishing pigs1

Boosted trees to predict pneumonia, growth, and meat percentage of growing-finishing pigs1

Prediction of Carcass Traits of Hair Sheep Lambs Using Body Measurements

Predicting carcass tissue composition in Blackbelly sheep using ultrasound measurements and machine learning methods

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