Cattle weight estimation using active contour models and regression trees Bagging

Vanessa Weber

doi:10.1016/j.compag.2020.105804

Cattle weight estimation using active contour models and regression trees Bagging

2020-12-01

38

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V

Vanessa Weber *

F

Fabricio de Lima Weber

G

Gilberto Astolfi

G

Geazy Vilharva Menezes

J

João Vitor de Andrade Porto

F

Fábio Prestes Cesar Rezende

P

Pedro Henrique de Moraes

E

Edson Takashi Matsubara

R

Rodrigo Gonçalves Mateus

T

Thiago Luís Alves Campos de Araújo

L

Luiz Otávio Campos da Silva

U

U. G. P. de Abreu

R

Rodrigo da Costa Gomes

H

Hemerson Pistori

DOI：10.1016/j.compag.2020.105804

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Abstract

En 中文

Monitoring the weight of beef cattle is important for productive strategies. The main goal of this work was to automatically extract measurements from 2D images of the dorsal area of Nellore cattle to estimate the weight of these cattle using regression algorithms. For this purpose, Euclidean distances from points generated by the Active Contour Model, together with features obtained from the dorsal Convex Hull, were selected. These were submitted to Bagging, Regression by Discretization and Random Forest algorithms for analysis of the predicted error metrics. The Bagging algorithm showed the best results, with Mean Absolute Error (MAE) of 13.44 kg (+/- 2.76), Square Root of the Mean Error (RMSE) of 15.88 kg (+/- 2.86), Mean Absolute Percentage Error (MAPE) of 2.27% and correlation coefficient at 0.75.

Keywords:

Computer vision

Livestock precision

Machine learning

Regression

Weight estimation

Cattle weight estimation using active contour models and regression trees Bagging

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