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المؤلفAyadi, Safa
المؤلفBen Said, Ahmed
المؤلفJabbar, Rateb
المؤلفAloulou, Chafik
المؤلفChabbouh, Achraf
المؤلفBen Achballah, Ahmed
تاريخ الإتاحة2023-10-08T08:41:46Z
تاريخ النشر2020
اسم المنشورCommunications in Computer and Information Science
المصدرScopus
الرقم المعياري الدولي للكتاب18650929
معرّف المصادر الموحدhttp://dx.doi.org/10.1007/978-3-030-65810-6_7
معرّف المصادر الموحدhttp://hdl.handle.net/10576/48317
الملخصCattle 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.
راعي المشروعAcknowledgment. This research work is supported by LifeEye LLC. The statements made herein are solely the responsibility of the authors.
اللغةen
الناشرSpringer Science and Business Media Deutschland GmbH
الموضوعAction recognition
Computer vision
Dairy cows
Deep learning
Machine learning
Rumination behavior
العنوانDairy Cow Rumination Detection: A Deep Learning Approach
النوعConference Paper
الصفحات123-139
رقم المجلد1348
dc.accessType Abstract Only


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