JRM Vol.30 No.2 pp. 206-213
doi: 10.20965/jrm.2018.p0206


Automatic Measurement and Determination of Body Condition Score of Cows Based on 3D Images Using CNN

Masahiro Shigeta, Reiichirou Ike, Hiroshi Takemura, and Hayato Ohwada

Tokyo University of Science
2641 Yamazaki, Noda, Chiba 278-8510, Japan

September 20, 2017
February 12, 2018
April 20, 2018
3D camera, dairy cow, deep learning, point cloud, BCS
Automatic Measurement and Determination of Body Condition Score of Cows Based on 3D Images Using CNN

Image used for CNN

According to the Ministry of Agriculture, Forestry, and Fisheries of Japan, the number of rearing houses has been decreasing in Japan in recent years due to lower business volumes. However, the number of rearing animals per house has been increasing, and in such situations, management of a herd of cows becomes very important. However, although systems such as a milking robot and an automatic feeding machine have been designed and implemented, an automatic measurement system to evaluate the body condition score (BCS), which is used for nutrition management of dairy cows, has not yet become popular. There have been many prior studies on this subject; however, none of them have succeeded in creating an inexpensive and highly accurate system that is capable of capturing images over a long period of time. The purpose of this study was to develop a system that continuously and automatically captures images of cows using a camera over a long period of time and to carry out a highly accurate determination of BCS. By attaching a three-dimensional camera to a sorting gate of a milking robot, we have developed a system that automatically captures images of cows as they pass through the gate. Data obtained from the captured images are automatically accumulated in a server. Thus, we successfully obtained a huge amount of data within two months. All parts of the image except the dairy cows were removed from the obtained three-dimensional data, and the three-dimensional data were then converted into two-dimensional images. Subsequently, the two-dimensional images were analyzed using a convolutional neural network, resulting in 89.1% of the answers being correct. When the acceptable error was ±0.25, the rate of correct answers is 94.6%, and the average absolute error, which is the average of the difference between the predicted BCS and the actual BCS for all test data, is 0.05. Although we used images that do not cover the entire body of the cow because of the position of the camera and the variation in captured parts (depending on images), we have successfully achieved a high accuracy. This promises that even higher accuracy can be achieved by automating the flow of data and carrying out the appropriate treatment of data to determine BCS.

Cite this article as:
M. Shigeta, R. Ike, H. Takemura, and H. Ohwada, “Automatic Measurement and Determination of Body Condition Score of Cows Based on 3D Images Using CNN,” J. Robot. Mechatron., Vol.30, No.2, pp. 206-213, 2018.
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Last updated on Jul. 06, 2018