Cattle weight estimation using active contour models and regression trees Bagging

被引：40

作者：

Moraes Weber, Vanessa Aparecida ^{[1
,4
]}

Weber, Fabricio de Lima ^{[2
,4
]}

Oliveira, Adair da Silva ^{[2
]}

Astolfi, Gilberto ^{[2
,6
]}

Menezes, Geazy Vilharva ^{[2
]}

Porto, Vitor de Andrade ^{[1
]}

Cesar Rezende, Fabio Prestes ^{[1
]}

de Moraes, Pedro Henrique ^{[2
]}

Matsubara, Edson Takashi ^{[2
]}

Mateus, Rodrigo Goncalves ^{[1
]}

Alves Campos de Araujo, Thiago Luis ^{[3
,5
]}

Campos da Silva, Luiz Otavio ^{[3
]}

Arguelho de Queiroz, Eduardo Quirino ^{[1
]}

Pinto de Abreu, Urbano Gomes ^{[1
,4
,7
]}

Gomes, Rodrigo da Costa ^{[3
]}

Pistori, Hemerson ^{[1
,2
]}

机构：

[1] Univ Catolica Dom Bosco UCDB, Av Tamandare, BR-6000 Campo Grande, MS, Brazil

[2] Fed Univ Mato Grosso UFMS, Fac Comp, POB 549,79-070-900, Campo Grande, MS, Brazil

[3] Brazilian Agr Res Corp Embrapa Beef Cattle, Av Radio Maia, BR-830 Campo Grande, MS, Brazil

[4] State Univ Mato Grosso Sul UEMS, Av Dom Antonio Barbosa, BR-4155 Campo Grande, MS, Brazil

[5] Fed Univ Ceara UFC, Fortaleza, CE, Brazil

[6] Fed Inst Educ Sci & Technol Mato Grosso IFMS, Campo Grande, MS, Brazil

[7] Brazilian Agr Res Corp Embrapa Pantanal, Corumba, MS, Brazil

来源：

COMPUTERS AND ELECTRONICS IN AGRICULTURE | 2020年 / 179卷

关键词：

Computer vision; Livestock precision; Machine learning; Regression; Weight estimation; COW BODY CONDITION; FEED-INTAKE; INFRARED THERMOGRAPHY; FACE RECOGNITION; MACHINE VISION; DAIRY-CATTLE; IMAGES; PREDICTION; BEHAVIOR; SYSTEM;

D O I：

10.1016/j.compag.2020.105804

中图分类号：

S [农业科学];

学科分类号：

09 ;

摘要：

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.

引用

页数：12

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