Application of computer vision and support vector regression for weight prediction of live broiler chicken

被引：23

作者：

Amraei S. ^{[1
]}

Abdanan Mehdizadeh S. ^{[1
]}

Sallary S. ^{[2
]}

机构：

[1] Department of Mechanics of Biosystems Engineering, Faculty of Agricultural Engineering and Rural Development, Ramin Agriculture and Natural Resources University of Khuzestan, Khuzestan

[2] Department of Animal Science, Faculty of Animal Science and Food Technology Ramin Agriculture and Natural Resources University of Khuzestan, Khuzestan

来源：

Engineering in Agriculture, Environment and Food | 2017年 / 10卷 / 04期

关键词：

Broiler; Image processing; Support vector regression; Weight estimation;

D O I：

10.1016/j.eaef.2017.04.003

中图分类号：

学科分类号：

摘要：

A very important ingredient in the recipe for a productive broiler breeder flock is the collection of frequent and accurate body weights. To achieve this goal in this paper image processing and support vector regression (SVR) were used as a non-invasive method. An ellipse fitting algorithm using generalized Hough transform was performed to localize chickens within the pen and the head as well as the tail of chickens was removed using Chan-Vese method. After that from broiler images six features were extracted, namely area, convex area, perimeter, eccentricity, major axis length and minor axis length. According to statistical analysis between weight estimation of SVR and manual measurement of birds up to 42 days, no significant difference was observed (P > 0.05). The RMSE (root mean square error), MAPE (mean absolute percentage error) and the R2 (correlation coefficient) value of SVR algorithm were 67.88, 8.63% and 0.98, respectively. This shows that machine vision along with SVR could promisingly estimate the weight of life broiler chickens. © 2017 Asian Agricultural and Biological Engineering Association

引用

页码：266 / 271

页数：5

共 40 条

[1]

Alonso J., Villa A., Bahamonde A., Improved estimation of bovine weight trajectories using support vector machine classification, Comput. Electron. Agric., 110, pp. 36-41, (2015)

[2]

Alonso J., Castanon A.R., Bahamonde A., Support Vector Regression to predict carcass weight in beef cattle in advance of the slaughter, Comput. Electron. Agric., 91, pp. 116-120, (2013)

[3]

Blokhuis H.J., Van der Haar J.W., Fuchs J.M.M., Do weighing figures represent the flock average, Poult. Int., 4, 5, pp. 17-19, (1988)

[4]

Brandl N., Jorgensen E., Determination of live weight of pigs from dimensions measured using image analysis, Comput. Electron. Agric., 15, 1, pp. 57-72, (1996)

[5]

Chang C., Lin C., {LIBSVM}: a Library for Support Vector Machines (Version 2.3), (2001)

[6]

Chen Y.R., Chao K., Kim M.S., Machine vision technology for agricultural applications, Comput. Electron. Agric., 36, 2, pp. 173-191, (2002)

[7]

Cherkassky E., Mulier F., Learning from Data: Concepts, Theory, and Methods, (1998)

[8]

Cherkassky V., Ma Y., Practical selection of SVM parameters and noise estimation for SVM regression, Neural Netw., 17, 2, pp. 113-126, (2004)

[9]

Cortes C., Vapnik V., Support-vector network, Mach. Learn., 20, 3, pp. 273-297, (1995)

[10]

Davies E.R., Finding ellipses using the generalised Hough transform, Pattern Recognit. Lett., 9, 2, pp. 87-96, (1989)

← 1 2 3 4 →