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

Yusei Kawagoe, Ikuo Kobayashi and Thi Thi Zin ()
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Yusei Kawagoe: Graduate School of Engineering, University of Miyazaki, Miyazaki 889-2192, Japan
Ikuo Kobayashi: Field Science Center, Faculty of Agriculture, University of Miyazaki, Miyazaki 889-2192, Japan
Thi Thi Zin: Graduate School of Engineering, University of Miyazaki, Miyazaki 889-2192, Japan

Agriculture, 2023, vol. 13, issue 5, 1-15

Abstract: 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.

Keywords: individual identification; cow face; feeding time estimation; feeding behavior detection; Hough transform; YOLO detector (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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