Facial Region Analysis for Individual Identification of Cows and Feeding Time Estimation

被引:8
作者
Kawagoe, Yusei [1 ]
Kobayashi, Ikuo [2 ]
Zin, Thi Thi [1 ]
机构
[1] Univ Miyazaki, Grad Sch Engn, Miyazaki 8892192, Japan
[2] Univ Miyazaki, Fac Agr, Field Sci Ctr, Miyazaki 8892192, Japan
来源
AGRICULTURE-BASEL | 2023年 / 13卷 / 05期
关键词
individual identification; cow face; feeding time estimation; feeding behavior detection; Hough transform; YOLO detector; BEHAVIOR;
D O I
10.3390/agriculture13051016
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
With the increasing number of cows per farmer in Japan, an automatic cow monitoring system is being introduced. One important aspect of such a system is the ability to identify individual cows and estimate their feeding time. In this study, we propose a method for achieving this goal through facial region analysis. We used a YOLO detector to extract the cow head region from video images captured during feeding with the head region cropped as a face region image. The face region image was used for cow identification and transfer learning was employed for identification. In the context of cow identification, transfer learning can be used to train a pre-existing deep neural network to recognize individual cows based on their unique physical characteristics, such as their head shape, markings, or ear tags. To estimate the time of feeding, we divided the feeding area into vertical strips for each cow and established a horizontal line just above the feeding materials to determine whether a cow was feeding or not by using Hough transform techniques. We tested our method using real-life data from a large farm, and the experimental results showed promise in achieving our objectives. This approach has the potential to diagnose diseases and movement disorders in cows and could provide valuable insights for farmers.
引用
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页数:15
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