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AuthorAyadi, Safa
AuthorBen Said, Ahmed
AuthorJabbar, Rateb
AuthorAloulou, Chafik
AuthorChabbouh, Achraf
AuthorBen Achballah, Ahmed
Available date2023-10-08T08:41:46Z
Publication Date2020
Publication NameCommunications in Computer and Information Science
ResourceScopus
ISSN18650929
URIhttp://dx.doi.org/10.1007/978-3-030-65810-6_7
URIhttp://hdl.handle.net/10576/48317
AbstractCattle activity is an essential index for monitoring health and welfare of the ruminants. Thus, changes in the livestock behavior are a critical indicator for early detection and prevention of several diseases. Rumination behavior is a significant variable for tracking the development and yield of animal husbandry. Therefore, various monitoring methods and measurement equipment have been used to assess cattle behavior. However, these modern attached devices are invasive, stressful and uncomfortable for the cattle and can influence negatively the welfare and diurnal behavior of the animal. Multiple research efforts addressed the problem of rumination detection by adopting new methods by relying on visual features. However, they only use few postures of the dairy cow to recognize the rumination or feeding behavior. In this study, we introduce an innovative monitoring method using Convolution Neural Network (CNN)-based deep learning models. The classification process is conducted under two main labels: ruminating and other, using all cow postures captured by the monitoring camera. Our proposed system is simple and easy-to-use which is able to capture long-term dynamics using a compacted representation of a video in a single 2D image. This method proved efficiency in recognizing the rumination behavior with 95%, 98% and 98% of average accuracy, recall and precision, respectively.
SponsorAcknowledgment. This research work is supported by LifeEye LLC. The statements made herein are solely the responsibility of the authors.
Languageen
PublisherSpringer Science and Business Media Deutschland GmbH
SubjectAction recognition
Computer vision
Dairy cows
Deep learning
Machine learning
Rumination behavior
TitleDairy Cow Rumination Detection: A Deep Learning Approach
TypeConference Paper
Pagination123-139
Volume Number1348
dc.accessType Abstract Only


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