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

• Published in: Agriculture (Basel) Vol. 13; no. 5; p. 1016
• Main Authors: Kawagoe, Yusei, Kobayashi, Ikuo, Zin, Thi Thi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2023
• Subjects: Accuracy; Agriculture; Algorithms; Appropriations; Artificial neural networks; Automation; Cameras; Cattle; cow face; Dairy farms; Detectors; Efficiency; Farmers; Farms; Feeding; feeding behavior detection; feeding time estimation; Head; Hough transform; Hough transformation; individual identification; Internet of Things; Livestock; Livestock industry; Machine learning; Monitoring systems; Movement disorders; Neural networks; Physical characteristics; Physical properties; Productivity; Radio frequency identification; Sensors; Transfer learning; YOLO detector; Japan
• ISSN: 2077-0472; 2077-0472
• DOI: 10.3390/agriculture13051016

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.