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https://hdl.handle.net/11147/14259
Title: | Estrus Detection and Dairy Cow Identification With Cascade Deep Learning for Augmented Reality-Ready Livestock Farming | Authors: | Arıkan, İ. Ayav, T. Seçkin, A.Ç. Soygazi, F. |
Keywords: | artificial intelligence augmented reality dairy cow identification deep learning estrus detection image processing livestock precision livestock farming transfer learning Augmented reality Dairies Deep learning Farms Image segmentation Losses Dairy cow Dairy cow identification Deep learning Estrus detection Images processing Livestock Precision livestock farming Region-of-interest Regions of interest Transfer learning Mountings animal artificial intelligence augmented reality bovine dairying deep learning estrus female livestock milk procedures Animals Artificial Intelligence Augmented Reality Cattle Dairying Deep Learning Estrus Detection Female Livestock Milk |
Publisher: | Multidisciplinary Digital Publishing Institute (MDPI) | Abstract: | Accurate prediction of the estrus period is crucial for optimizing insemination efficiency and reducing costs in animal husbandry, a vital sector for global food production. Precise estrus period determination is essential to avoid economic losses, such as milk production reductions, delayed calf births, and disqualification from government support. The proposed method integrates estrus period detection with cow identification using augmented reality (AR). It initiates deep learning-based mounting detection, followed by identifying the mounting region of interest (ROI) using YOLOv5. The ROI is then cropped with padding, and cow ID detection is executed using YOLOv5 on the cropped ROI. The system subsequently records the identified cow IDs. The proposed system accurately detects mounting behavior with 99% accuracy, identifies the ROI where mounting occurs with 98% accuracy, and detects the mounting couple with 94% accuracy. The high success of all operations with the proposed system demonstrates its potential contribution to AR and artificial intelligence applications in livestock farming. © 2023 by the authors. | URI: | https://doi.org/10.3390/s23249795 https://hdl.handle.net/11147/14259 |
ISSN: | 1424-8220 |
Appears in Collections: | PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection |
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