Alexandre Cominotte
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Alexandre Cominotte has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Arthur Francisco Araújo Fernandes
Cobb-Vantress Inc
1 shared publication - Guilherme Jordão Magalhães Rosa
Department of Animal and Dairy Sciences University of Wisconsin‐Madison Madison Wisconsin USA
1 shared publication - Guilherme Luis Pereira
Department of Animal Breeding and Nutrition, School of Veterinary e Animal Science (FMVZ), São Paulo State University (Unesp), Botucatu 18618-681, SP, Brazil
1 shared publication - Joao Ricardo Reboucas Dorea
Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA
1 shared publication - Otávio Rodrigues Machado Neto
School of Agricultural and Veterinarian Science São Paulo State University Jaboticabal Brazil
1 shared publication - Rodrigo Torres
School of Veterinary and Animal Science, São Paulo State University, Botucatu 18618-681, SP, Brazil
1 shared publication
Latest Publications
- Open Access
Use of Biometric Images to Predict Body Weight and Hot Carcass Weight of Nellore Cattle
The objective of this study was to evaluate different methods of predicting body weight (BW) and hot carcass weight (HCW) from biometric measurements obtained through three-dimensional images of Nellore cattle. We collected BW and HCW of 1350 male Nellore cattle (bulls and steers) from four different experiments. Three-dimensional images of each animal were obtained using the Kinect® model 1473 sensor (Microsoft Corporation, Redmond, WA, USA). Models were compared based on root mean square error estimation and concordance correlation coefficient. The predictive quality of the approaches used multiple linear regression (MLR); least absolute shrinkage and selection operator (LASSO); partial least square (PLS), and artificial neutral network (ANN) and was affected not only by the conditions (set) but also by the objective (BW vs. HCW). The most stable for BW was the ANN (Set 1: RMSEP = 19.68; CCC = 0.73; Set 2: RMSEP = 27.22; CCC = 0.66; Set 3: RMSEP = 27.23; CCC = 0.70; Set 4: RMSEP = 33.74; CCC = 0.74), which showed predictive quality regardless of the set analyzed. However, when evaluating predictive quality for HCW, the models obtained by LASSO and PLS showed greater quality over the different sets. Overall, the use of three-dimensional images was able to predict BW and HCW in Nellore cattle.
Open AccessUse of Biometric Images to Predict Body Weight and Hot Carcass Weight of Nellore Cattle
The objective of this study was to evaluate different methods of predicting body weight (BW) and hot carcass weight (HCW) from biometric measurements obtained through three-dimensional images of Nellore cattle. We collected BW and HCW of 1350 male Nellore cattle (bulls and steers) from four different experiments. Three-dimensional images of each animal were obtained using the Kinect® model 1473 sensor (Microsoft Corporation, Redmond, WA, USA). Models were compared based on root mean square error estimation and concordance correlation coefficient. The predictive quality of the approaches used multiple linear regression (MLR); least absolute shrinkage and selection operator (LASSO); partial least square (PLS), and artificial neutral network (ANN) and was affected not only by the conditions (set) but also by the objective (BW vs. HCW). The most stable for BW was the ANN (Set 1: RMSEP = 19.68; CCC = 0.73; Set 2: RMSEP = 27.22; CCC = 0.66; Set 3: RMSEP = 27.23; CCC = 0.70; Set 4: RMSEP = 33.74; CCC = 0.74), which showed predictive quality regardless of the set analyzed. However, when evaluating predictive quality for HCW, the models obtained by LASSO and PLS showed greater quality over the different sets. Overall, the use of three-dimensional images was able to predict BW and HCW in Nellore cattle.